Apparatus and methods for training of robots

ABSTRACT

A random k-nearest neighbors (RKNN) approach may be used for regression/classification model wherein the input includes the k closest training examples in the feature space. The RKNN process may utilize video images as input in order to predict motor command for controlling navigation of a robot. In some implementations of robotic vision based navigation, the input space may be highly dimensional and highly redundant. When visual inputs are augmented with data of another modality that is characterized by fewer dimensions (e.g., audio), the visual data may overwhelm lower-dimension data. The RKNN process may partition available data into subsets comprising a given number of samples from the lower-dimension data. Outputs associated with individual subsets may be combined (e.g., averaged). Selection of number of neighbors, subset size and/or number of subsets may be used to trade-off between speed and accuracy of the prediction.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is claims priority to co-pending and co-owned U.S.Provisional Patent Application Ser. No. 62/059,039 entitled “LEARNINGAPPARATUS AND METHODS”, filed Oct. 2, 2014, which is incorporated hereinby reference in its entirety. This application is related to co-pendingand co-owned U.S. patent application Serial No. 14/244,890 entitled“LEARNING APPARATUS AND METHODS FOR CONTROL OF ROBOTIC DEVICES”, filedApr. 3, 2014, Ser. No. 13/918,338 entitled “ROBOTIC TRAINING APPARATUSAND METHODS”, filed Jun. 14, 2013, U.S. patent application Ser. No.13/918,298 entitled “HIERARCHICAL ROBOTIC CONTROLLER APPARATUS Thisapplication is related to co-pending and co-owned U.S. patentapplication Ser. No. 14/244,890 entitled “LEARNING APPARATUS AND METHODSFOR CONTROL OF ROBOTIC DEVICES”, filed Apr. 3, 2014, Ser. No. 13/918,338entitled “ROBOTIC TRAINING APPARATUS AND METHODS”, filed Jun. 14, 2013,U.S. patent application Ser. No. 13/918,298 entitled “HIERARCHICALROBOTIC CONTROLLER APPARATUS AND METHODS”, filed Jun. 14, 2013, Ser. No.13/907,734 entitled “ADAPTIVE ROBOTIC INTERFACE APPARATUS AND METHODS”,filed May 31, 2013, Ser. No. 13/842,530 entitled “ADAPTIVE PREDICTORAPPARATUS AND METHODS”, filed Mar. 15, 2013, Ser. No. 13/842,562entitled “ADAPTIVE PREDICTOR APPARATUS AND METHODS FOR ROBOTIC CONTROL”,filed Mar. 15, 2013, Ser. No. 13/842,616 entitled “ROBOTIC APPARATUS ANDMETHODS FOR DEVELOPING A HIERARCHY OF MOTOR PRIMITIVES”, filed Mar. 15,2013, Ser. No. 13/842,647 entitled “MULTICHANNEL ROBOTIC CONTROLLERAPPARATUS AND METHODS”, filed Mar. 15, 2013, and Ser. No. 13/842,583entitled “APPARATUS AND METHODS FOR TRAINING OF ROBOTIC DEVICES”, filedMar. 15, 2013, each of the foregoing being incorporated herein byreference in its entirety.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

FIELD OF THE DISCLOSURE

The present disclosure relates to, inter alia, computerized apparatusand methods for training of robotic devices to perform various tasks.

BACKGROUND

Users may train a robot with supervised learning to perform a task(e.g., navigation, manipulation, and/or other tasks). Users may want toknow what is the performance of the robot while executing a task (e.g.,does it bump into obstacles, how precise is manipulation, and/or othermeasurements of performance). If a user knows about a robot'sperformance, he may speed up training process or make training moreprecise. Users may allow a robot to perform the task autonomously, butthat may be expensive (e.g., time consuming, robot can damage itself,and/or other expenses or disadvantages).

Conventionally, users may train a robot with supervised learning toperform a task (e.g., navigation, manipulation, and/or other tasks).Tasks may vary in complexity. Depending on a task's complexity,prediction performance of a trained classifier may vary. For simpletasks such as navigating while avoiding obstacles and grasping a target,following a trajectory to the target may be a situation in which asingle classifier can achieve acceptable performance. Using existingapproaches, for more complex tasks such as fetching an object orsequential manipulation tasks (e.g., take a spoon, stir a soup, and thenput spoon back), a single classifier may be unable to infer necessaryactions from demonstrations alone.

Random K nearest neighbor (RKNN) methods may be used in classificationand/or a general regression applications wherein the input space is bothhigh dimensional and highly redundant, the most common example of thisbeing when RGB images may be used as input and the model output is thepredicted motor command. However, when the input data are multimodal(i.e. consisting of data from more than one sensory modality) and thedimensionality of one modality is much greater than the dimensionalityof another traditional RKNN approaches may prove inadequate forproviding control signals that accurately reflect the smaller modalitysignal content.

According to conventional approaches, given a task in which a user wantsto train a robot to navigate along a path from location A to location B(A and B may be the same location, in which case the path takes the formof a loop), the user may first control the robot one time or multipletimes to move along the desired path. This may constitute the trainingof the robot. Thereafter, the robot may be expected to perform the samenavigation autonomously.

One typical approach may be to store the motor commands that wereexecuted during the training phase, and then simply replay them. This,however, may not work well in practice, at least because there may besome variability in how the motor commands translate into actualmovement in physical space. In general, if the robot is slightly offcourse, it may continue to drift more and more off course.

SUMMARY

One aspect of the disclosure relates to a method of determining acontrol signal for a robot. The method may be performed by a specialpurpose computing platform having one or more processors executinginstructions stored by a non-transitory computer-readable storagemedium. The method may comprise receiving first input features of firsttype and second input features of a second type. The method may comprisedetermining a subset of features by randomly selecting a portion of thefirst input features and at least one feature from the second inputfeatures. The method may comprise comparing individual features of thesubset to corresponding features of a plurality of training featuresets. Individual ones of the plurality of training feature sets maycomprise a number of training features. The number may be equal to orgreater than the quantity of features within the subset of features. Themethod may comprise, based on the comparison, determining a similaritymeasure for a given training set of the plurality of training featuresets. The similarity measure may characterize similarity betweenfeatures of the subset and features of the given training set. Themethod may comprise, responsive to the similarity measure breaching athreshold, selecting one or more training sets from the plurality oftraining sets. The method may comprise determining one or more potentialcontrol signals for the robot. Individual ones of the one or morepotential control signals may be associated with a correspondingtraining set of the plurality of training sets. The method may comprisedetermining the control signal based on a transformation obtained fromthe one or more potential control signals. Individual ones of theplurality of training feature sets may comprise features of the firsttype and at least one feature of the second type. Individual ones of theplurality of training feature sets may be obtained during trainingoperation of the robot. The training operation may be performedresponsive to receiving a training signal from the robot. Individualones of the one or more potential control signals may be determinedbased on the training signal and the features of the given training set.

In some implementations, the similarity measure may be determined basedon a difference between values of the features of the subset and valuesof the features of the given training set.

In some implementations, the similarity measure may be determined basedon a distance metric between individual features of the subset offeatures and corresponding features of the given training set.

In some implementations, selecting one or more training sets maycomprise selecting a training set associated with a smallest distancemetric.

In some implementations, selecting one or more training sets maycomprise selecting N training sets associated with a lowest percentileof the distance metric. N may be greater than two.

In some implementations, the transformation may comprise a statisticaloperation performed on individual ones of the one or more potentialcontrol signals associated with the selected N training sets.

In some implementation, the statistical operation may be selected fromthe group including mean and percentile.

In some implementations, the transformation may comprise a weighted sumof a product of individual ones of the one or more potential controlsignals and a corresponding distance measure associated with theselected N training sets.

In some implementations, the control signal may be configured to causethe robot to execute the action. The first input type may comprise adigital image comprising a plurality of pixel values. The second inputtype may comprise a binary indication associated with the action beingexecuted.

In some implementations, the training may comprise a plurality ofiterations configured based on the training signal. A given iterationmay be characterized by a control command and a performance measureassociated with the action execution based on the control command.

In some implementations, the plurality of pixels may comprise at least10 pixels. The random selection may be performed based on a randomnumber generation operation.

Another aspect of the disclosure relates to a self-contained roboticapparatus. The apparatus may comprise a platform comprising a motor. Theapparatus may comprise a first sensor component configured to provide asignal conveying a video frame comprising a plurality of pixels. Theapparatus may comprise a second sensor component configured to provide abinary sensor signal characterized by one of two states. The apparatusmay comprise a memory component configured to store training sets. Agiven training set may comprise an instance of the video frame, aninstance of the binary signal, and an instance of a motor controlindication configured to cause the robot to execute an action. Theapparatus may comprise one or more processors configured to operate arandom k-nearest neighbors learning process to determine a motor controlindication by at least: determining a subset of features comprising thebinary signal and a set of pixels randomly selected from the pluralityof pixels; scaling individual pixels of the set of pixels by a scalingfactor; scaling features of the subset by a scaling factor; comparingindividual scaled features of the subset to corresponding features ofindividual ones of the training sets; based on the comparison,determining a similarity measure for a given training set, thesimilarity measure characterizing similarity between features of thesubset and features of the given training set; based on an evaluation ofthe similarity measure, selecting one or more of the training sets;determining one or more potential control signals for the robot,individual ones of the one or more potential control signals beingassociated with a corresponding training set; and determining thecontrol signal based on a transformation obtained from the one or morepotential control signals. Individual ones of the plurality of trainingfeature sets may comprise features of the first type and at least onefeature of the second type. Individual ones of the plurality of trainingfeature sets may be obtained during training operation of the robot. Thetraining operation may be performed responsive to receiving a trainingsignal from the robot. Individual ones of the one or more potentialcontrol signals may be determined based on the training signal and thefeatures of the given training set.

In some implementations, scaling may comprise a multiplication of thefirst input by the scaling factor. The scaling factor may be determinedbased on a number of pixels in the subset.

In some implementations, the scaling factor may be determined based on aratio of a range of pixel values to a range of the binary values.

In some implementations, action may comprisetarget-approach-obstacle-avoidance. Scaling may be performed based on asize of obstacle or object as it appears in the video frame.

In some implementations, scaling may be pixel specific.

Yet another aspect of the disclosure relates to a non-transitorycomputer-readable storage medium having instructions embodied thereon.The instructions may be executable by a processor to perform a method ofselecting an outcome of a plurality of outcomes. The method may comprisedetermining a history of sensory input. The method may comprise applyinga transformation to an instance of the sensory input. The transformationmay be configured, based on analysis of the history, to produce scaledinput. The method may comprise determining a set of features comprisingfeatures of a first type randomly selected from the scaled input and atleast one feature of a second type. The method may comprise comparingindividual features of the set to corresponding features of a pluralityof training feature sets. Individual ones of the plurality of trainingfeature sets may comprise a number of training features. The number maybe equal to or greater than the quantity of features within the set offeatures. The method may comprise, based on the comparison, determininga similarity measure for a given training set of the plurality oftraining feature sets. The similarity measure may characterizesimilarity between features of the subset and features of the giventraining set. The method may comprise, responsive to the similaritymeasure breaching a threshold, selecting one or more training sets fromthe plurality of training sets. The method may comprise determining oneor more potential control signals for the robot. Individual ones of theone or more potential control signals may be associated with acorresponding training set of the plurality of training sets. The methodmay comprise determining the control signal based on a transformationobtained from the one or more potential control signals. Individual onesof the plurality of training feature sets may comprise features of thefirst type and at least one feature of the second type. Individual onesof the plurality of training feature sets may be obtained duringtraining operation of the robot. The training operation may be performedresponsive to receiving a training signal from the robot. Individualones of the one or more potential control signals may be determinedbased on the training signal and the features of the given training set.

In some implementations, the analysis of the history may comprise adetermination of feature mean and feature standard deviation. Thetransformation may comprise subtracting the feature mean and dividingthe outcome by the feature standard deviation.

In some implementations, the set may comprise a plurality of setfeatures. Individual ones of the set features may be characterized by apointer. The feature mean and the feature standard deviation may beconfigured for a respective pointer.

In some implementations, the input of the first type may comprise amatrix of values. The pointer may identify a value within the matrix.The feature mean and the feature standard deviation may be configuredfor a given location within the matrix.

These and other objects, features, and characteristics of the systemand/or method disclosed herein, as well as the methods of operation andfunctions of the related elements of structure and the combination ofparts and economies of manufacture, will become more apparent uponconsideration of the following description and the appended claims withreference to the accompanying drawings, all of which form a part of thisspecification, wherein like reference numerals designate correspondingparts in the various figures. It is to be expressly understood, however,that the drawings are for the purpose of illustration and descriptiononly and are not intended as a definition of the limits of thedisclosure. As used in the specification and in the claims, the singularform of “a”, “an”, and “the” include plural referents unless the contextclearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graphical illustration depicting a robotic apparatuscomprising an adaptive controller configured for autonomous navigation,in accordance with one or more implementations.

FIG. 2 is functional block diagram illustrating persistent switchingapparatus, according to some implementations, according to one or moreimplementations.

FIG. 3A is a plot illustrating nonlinear dependence of absolute cost asa function of time during training, according to one or moreimplementations.

FIG. 3B is a plot illustrating relative cost as a function of timeduring training, according to one or more implementations.

FIG. 4A is a block diagram illustrating an adaptive control system foruse with, e.g., the robotic apparatus of FIG. 1, according to one ormore implementations.

FIG. 4B is a block diagram illustrating an adaptive controller apparatuscomprising a mode combiner for use with, e.g., the robotic apparatus ofFIG. 1, according to one or more implementations.

FIG. 5 is a functional block diagram illustrating use of a timelinecomprising multiple bookmarks for implementing training undofunctionality, according to one or more implementations.

FIG. 6 is a functional block diagram depicting a computerized dataprocessing system configured for salient feature detection, according toone or more implementations.

FIG. 7 is a functional block diagram depicting a system comprisingsalient feature detection apparatus, according to one or moreimplementations.

FIG. 8 is a functional block diagram depicting the salient featuredetection apparatus of, e.g., FIG. 7, according to one or moreimplementations.

FIG. 9 is a functional block diagram depicting a fetch switchingcomputerized apparatus, according to one or more implementations.

FIGS. 10A-10D illustrate BrainOS system comprising action selectionmechanism, according to one or more implementations.

FIGS. 11A-11B present functional block diagrams depicting hierarchicallearning architecture of the BrainOS, according to one or moreimplementations.

FIGS. 12A-12B are graphical illustrations depicting touchfader userinterface for implementing supervised training of BrainOS, according toone or more implementations.

FIG. 13 is a graphical illustration depicting a mechanical touchfaderuser interface, according to one or more implementations.

FIG. 14 is a block diagram illustrating selection of a plurality ofsubsets configured using mandatory feature RKNN approach according toone or more implementations.

FIG. 15 illustrates determination of a predicted output by an RKNNclassifier apparatus, according to one or more implementations.

FIGS. 16A-16D illustrate use of gestures by a human operator forcommunicating control indications to a robotic device, in accordancewith one or more implementations.

FIG. 17 is a graphical illustration depicting an exemplary unmannedrobotic apparatus comprising salient feature determination apparatus ofthe disclosure configured for autonomous navigation, in accordance withone or more implementations.

FIG. 18 presents one exemplary implementation of a correction screenwith Listen mode activated, and Override Correct and autonomous modeavailable from the teacher control screen, in accordance with one ormore implementations.

FIG. 19 presents one exemplary implementation of operational sequencefor a learning robotic device, in accordance with one or moreimplementations.

FIG. 20 presents exemplary images for use with training path navigation,in accordance with one or more implementations.

FIG. 21 According to some implementations, the image new may be thecamera image seen during autonomous behavior. The image ‘old’ may be theclosest match found in the buffer of training images. The image ‘shift’may show how the ‘new’ image is shifted to obtain a good match with the‘old’ image. The amount of shift in x and y direction may be determinedby a phase correlation calculation. With the images on the left side,the camera image may be shifted left, which may imply the robot needs toadjust its heading right. With the images on the right side, it is theother way around.

FIG. 22 is a plot presenting data related to the sequence number of theimage from the training buffer chosen to be the most likely match as afunction of time.

FIG. 23 is a plot presenting a plot of the sequence number of the imagein the training buffer that the particle is pointing to as a function oftime.

FIG. 24 presents a logical flow diagram describing operations of the VORprocess, in accordance with one or more implementations.

FIG. 25 is an exemplary code listing that may be utilized with atwo-wheeled, self-balancing, robotic platform (e.g., similar to aSegway-type configuration), compensating for pan and tilt, in accordancewith one or more implementations.

All Figures disclosed herein are © Copyright 2014 Brain Corporation. Allrights reserved.

DETAILED DESCRIPTION

Implementations of the present disclosure will now be described indetail with reference to the drawings, which are provided asillustrative examples so as to enable those skilled in the art topractice the present technology. Notably, the figures and examples beloware not meant to limit the scope of the present disclosure to a singleimplementation, but other implementations are possible by way ofinterchange of or combination with some or all of the described orillustrated elements. Wherever convenient, the same reference numberswill be used throughout the drawings to refer to same or like parts.

Although the system(s) and/or methods) of this disclosure have beendescribed in detail for the purpose of illustration based on what iscurrently considered to be the most practical and preferredimplementations, it is to be understood that such detail is solely forthat purpose and that the disclosure is not limited to the disclosedimplementations, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present disclosure contemplates that, to the extent possible, one ormore features of any implementation may be combined with one or morefeatures of any other implementation

In the present disclosure, an implementation showing a singularcomponent should not be considered limiting; rather, the disclosure isintended to encompass other implementations including a plurality of thesame component, and vice-versa, unless explicitly stated otherwiseherein.

Further, the present disclosure encompasses present and future knownequivalents to the components referred to herein by way of illustration.

As used herein, the term “bus” is meant generally to denote all types ofinterconnection or communication architecture that is used to access thesynaptic and neuron memory. The “bus” could be optical, wireless,infrared or another type of communication medium. The exact topology ofthe bus could be for example standard “bus”, hierarchical bus,net-work-on-chip, address-event-representation (AER) connection, orother type of communication topology used for accessing, e.g., differentmemories in pulse-based system.

As used herein, the terms “computer”, “computing device”, and“computerized device”, include, but are not limited to, personalcomputers (PCs) and minicomputers, whether desktop, laptop, orotherwise, mainframe computers, workstations, servers, personal digitalassistants (PDAs), handheld computers, embedded computers, programmablelogic device, personal communicators, tablet or “phablet” computers,portable navigation aids, J2ME equipped devices, smart TVs, cellulartelephones, smart phones, personal integrated communication orentertainment devices, or literally any other device capable ofexecuting a set of instructions and processing an incoming data signal.

As used herein, the term “computer program” or “software” is meant toinclude any sequence or human or machine cognizable steps which performa function. Such program may be rendered in virtually any programminglanguage or environment including, for example, C/C++, C#, Fortran,COBOL, MATLAB™, PASCAL, Python, assembly language, markup languages(e.g., HTML, SGML, XML, VoXML), and the like, as well as object-orientedenvironments such as the Common Object Request Broker Architecture(CORBA), Java™ (including J2ME, Java Beans), Binary Runtime Environment(e.g., BREW), and other languages.

As used herein, the terms “connection”, “link”, “synaptic channel”,“transmission channel”, “delay line”, are meant generally to denote acausal link between any two or more entities (whether physical orlogical/virtual), which enables information exchange between theentities.

As used herein the term feature may refer to a representation of anobject edge, determined by change in color, luminance, brightness,transparency, texture, and/or curvature. The object features maycomprise, inter alia, individual edges, intersections of edges (such ascorners), orifices, and/or curvature

As used herein, the term “memory” includes any type of integratedcircuit or other storage device adapted for storing digital dataincluding, without limitation, ROM. PROM, EEPROM, DRAM, Mobile DRAM,SDRAM, DDR/2 SDRAM, EDO/FPMS, RLDRAM, SRAM, “flash” memory (e.g.,NAND/NOR), memristor memory, and PSRAM.

As used herein, the terms “processor”, “microprocessor” and “digitalprocessor” are meant generally to include all types of digitalprocessing devices including, without limitation, digital signalprocessors (DSPs), reduced instruction set computers (RISC),general-purpose (CISC) processors, microprocessors, gate arrays (e.g.,field programmable gate arrays (FPGAs)), PLDs, reconfigurable computerfabrics (RCFs), array processors, secure microprocessors, andapplication-specific integrated circuits (ASICs). Such digitalprocessors may be contained on a single unitary IC die, or distributedacross multiple components.

As used herein, the term “network interface” refers to any signal, data,or software interface with a component, network or process including,without limitation, those of the FireWire (e.g., FW400, FW800, and/orother FireWire implementation.), USB (e.g., USB2), Ethernet (e.g.,10/100, 10/100/1000 (Gigabit Ethernet), 10-Gig-E, etc.), MoCA, Coaxsys(e.g., TVnet™), radio frequency tuner (e.g., in-band or OOB, cablemoderm, etc.), Wi-Fi (802.11), WiMAX (802.16), PAN (e.g., 802.15),cellular (e.g., 3G, LTE/LTE-A/TD-LTE, GSM, and/or other cellularinterface implementation) or IrDA families.

As used herein, the terms “pulse”, “spike”, “burst of spikes”, and“pulse train” are meant generally to refer to, without limitation, anytype of a pulsed signal, e.g., a rapid change in some characteristic ofa signal, e.g., amplitude, intensity, phase or frequency, from abaseline value to a higher or lower value, followed by a rapid return tothe baseline value and may refer to any of a single spike, a burst ofspikes, an electronic pulse, a pulse in voltage, a pulse in electricalcurrent, a software representation of a pulse and/or burst of pulses, asoftware message representing a discrete pulsed event, and any otherpulse or pulse type associated with a discrete information transmissionsystem or mechanism.

As used herein, the term “receptive field” is used to describe sets ofweighted inputs from filtered input elements, where the weights may beadjusted.

As used herein, the term “Wi-Fi” refers to, without limitation, any ofthe variants of IEEE-Std. 802.11 or related standards including 802.11a/b/g/n/s/v and 802.11-2012.

As used herein, the term “wireless” means any wireless signal, data,communication, or other interface including without limitation Wi-Fi,Bluetooth, 3G (3GPP/3GPP2), HSDPA/HSUPA, TDMA, CDMA (e.g., IS-95A,WCDMA, and/or other wireless interface implementation.), FHSS, DSSS,GSM, PAN/802.15, WiMAX (802.16), 802.20, narrowband/FDMA, OFDM, PCS/DCS,LTE/LTE-A/TD-LTE, analog cellular, CDPD, RFID or NFC (e.g., EPC GlobalGen. 2, ISO 14443, ISO 18000-3), satellite systems, millimeter wave ormicrowave systems, acoustic, and infrared (e.g., IrDA).

FIG. 1 depicts a mobile robotic apparatus that may be configured with anadaptive controller in accordance with one or more implementations ofe.g., the learning apparatuses illustrated in FIGS. 4A-4B, infra. Therobotic apparatus 160 may comprise a sensor component 166. The sensorcomponent 166 may be characterized by an aperture/field of view 168(e.g., an extent of the observable world that may be captured by thesensor at a given moment). The sensor component 166 may provideinformation associated with objects within the field-of-view 168. In oneor more implementations, such as object recognition, and/or obstacleavoidance, the output provided by the sensor component 166 may comprisea stream of pixel values associated with one or more digital images. Inone or more implementations of e.g., video, radar, sonography, x-ray,magnetic resonance imaging, and/or other types of sensing, the sensor166 output may be based on electromagnetic waves (e.g., visible light,infrared (IR), ultraviolet (UV), and/or other types of electromagneticwaves) entering an imaging sensor array. In some implementations, theimaging sensor array may comprise one or more of artificial retinalganglion cells (RGCs), a charge coupled device (CCD), an active-pixelsensor (APS), and/or other sensors. The input signal may comprise asequence of images and/or image frames. The sequence of images and/orimage frame may be received from a CCD camera via a receiver apparatusand/or downloaded from a file. The image may comprise a two-dimensionalmatrix of red/green/blue (RGB) values refreshed at a 25 Hz frame rate.It will be appreciated by those skilled in the arts that the above imageparameters are merely exemplary, and many other image representations(e.g., bitmap, CMYK, HSV, HSL, grayscale, and/or other representations)and/or frame rates are equally useful with the present disclosure.Pixels and/or groups of pixels associated with objects and/or featuresin the input frames may be encoded using, for example, latency encodingdescribed in co-owned U.S. patent application Ser. No. 12/869,583, filedAug. 26, 2010 and entitled “INVARIANT PULSE LATENCY CODING SYSTEMS ANDMETHODS”; U.S. Pat. No. 8,315,305, issued Nov. 20, 2012, and entitled“SYSTEMS AND METHODS FOR INVARIANT PULSE LATENCY CODING”; U.S. patentapplication Ser. No. 13/152,084, filed Jun. 2, 2011, and entitled“APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION”;and/or latency encoding comprising a temporal winner take all mechanismdescribed in U.S. patent application Ser. No. 13/757,607, filed Feb. 1,2013, and entitled “TEMPORAL WINNER TAKES ALL SPIKING NEURON NETWORKSENSORY PROCESSING APPARATUS AND METHODS”, each of the foregoing beingincorporated herein by reference in its entirety.

In one or more implementations, object recognition and/or classificationmay be implemented using a spiking neuron classifier comprisingconditionally independent subsets as described in co-owned U.S. patentapplication Ser. No. 13/756,372 filed Jan. 31, 2013, and entitled“SPIKING NEURON CLASSIFIER APPARATUS AND METHODS” and/or co-owned U.S.patent application Ser. No. 13/756,382 filed Jan. 31, 2013, and entitled“REDUCED LATENCY SPIKING NEURON CLASSIFIER APPARATUS AND METHODS”, eachof the foregoing being incorporated herein by reference in its entirety.

In one or more implementations, encoding may comprise adaptiveadjustment of neuron parameters, such as neuron excitability which isdescribed in U.S. patent application Ser. No. 13/623,820 entitled“APPARATUS AND METHODS FOR ENCODING OF SENSORY DATA USING ARTIFICIALSPIKING NEURONS”, filed Sep. 20, 2012, the foregoing being incorporatedherein by reference in its entirety.

In some implementations, analog inputs may be converted into spikesusing, for example, kernel expansion techniques described in co-ownedU.S. patent application Ser. No. 13/623,842 filed Sep. 20, 2012, andentitled “SPIKING NEURON NETWORK ADAPTIVE CONTROL APPARATUS ANDMETHODS”, the foregoing being incorporated herein by reference in itsentirety. The term continuous signal may be used to describe anon-spiking signal (e.g., analog, nary digital signal characterized byn-bits of resolution, n>1). In one or more implementations, analogand/or spiking inputs may be processed by mixed signal spiking neurons,such as co-owned U.S. patent application Ser. No. 13/313,826 entitled“APPARATUS AND METHODS FOR IMPLEMENTING LEARNING FOR ANALOG AND SPIKINGSIGNALS IN ARTIFICIAL NEURAL NETWORKS”, filed Dec. 7, 2011, and/orco-owned U.S. patent application Ser. No. 13/761,090 entitled “APPARATUSAND METHODS FOR IMPLEMENTING LEARNING FOR ANALOG AND SPIKING SIGNALS INARTIFICIAL NEURAL NETWORKS”, filed Feb. 6, 2013, each of the foregoingbeing incorporated herein by reference in its entirety.

In some implementations of robotic navigation in an arbitraryenvironment, the sensor component 166 may comprise a camera configuredto provide an output comprising a plurality of digital image framesrefreshed at, e.g., 25 Hz frame rate. The sensor output may be processedby a learning controller, e.g., as illustrated and described withrespect to FIG. 4A.

In some implementations of robotic vehicle navigation, output of thesensor 166 in FIG. 1 may comprise representations of one or more objects(e.g., targets, and/or obstacles). The tasks of the robot may beconfigured based on a context. In one or more implementations, thecontext may comprise one or more of robot state (e.g., location ormotion information, (position, orientation, speed), platform state orconfiguration (e.g., manipulator size and/or position), available powerand/or other), state of the environment (e.g., object size, location),environmental state (wind, rain), previous state information (e.g.,based on historic states of robot motions), and/or other characteristicstate information.

Persistent switcher apparatus and methods are disclosed herein, inaccordance with one or more implementations. Exemplary implementationsmay completely or partially alleviate this problem by using a hierarchyof behaviors and a stateful switcher. The switcher may learn whatsensory contexts should be associated with changes in behavior, and whatcontexts shouldn't. The example task may then be trained with simplepredictors based on the immediate visual input. Human operator knowledgeabout how to best divide a task in elementary behaviors may beleveraged.

In some implementations, a user (e.g., human operator) may train thesystem to switch between tasks based on the sensory context. FIG. 2shows a diagram of the system, according to some implementations. Apredictor may assign priorities to the available tasks based on thesensory context; the priorities may be trained by the user via acorrector plus combiner scheme. The predicted priorities may be filteredby a “persistent winner-take-all” module that only switches to adifferent task if the priority breaches a certain threshold.

FIG. 2 illustrates the task selector, in accordance with one or moreimplementations. The task selector may receive sensory context (e.g.,camera input and/or input from other sensors, other context signalsbased on internal states or system history (which may be processed toextract interesting features), and/or other information associated withsensory context) and control signals (e.g., “corrections”) from the uservia a remote controller and/or other control device. En someimplementations, the output of the system may be a choice among theavailable tasks to perform, frame by frame.

The predictor may be configured to output a vector of real values.Individual ones of those values may correspond to a possible task toperform. These values may be interpreted as the “priorities” of thedifferent tasks. In some implementations, the priorities may benon-negative and add up to 1 (e.g., via a soft-max layer).

In some implementations, the predictor may output one value for eachpossible pair of tasks to switch between (e.g., there may be m² outputsfor in available tasks). This may be useful when a given context needsto be associated to different tasks depending on the task currentlybeing performed.

The user may correct the system by providing an indication as to whichtask the system should be performing in a certain context. Assuming thatthe combiner (see below) is of the “overriding” type, user correctionsmay come as a vector with as many elements as tasks, with value “0” forall elements except “1” for the element corresponding to the task to beassociated with the context.

In some contexts the user may want to signify that the system should notswitch from whatever task it is performing. In some implementations,this may be done (e.g., in the case of an overriding combiner) bysending a vector of corrections with uniform values not breaching thethreshold of the “persistent winner takes all” block. If all thepredictor outputs do not breach the threshold in a given context, the“Persistent WTA” block may keep selecting the same task (see below).

The user corrections may be processed in specific ways before enteringthe combiner depending on the type of combiner and predictor used. Forexample, if the combiner is overriding and the predictor is a neuralnetwork with softmax output, it may be preferable to send [0.9, 0.05,0.05] instead of [1, 0, 0] as a correction vector to avoid driving thenetwork to saturation.

In some implementations, the combiner for this system may include theoverride combiner. Responsive to the user sending a correction, thecombiner may output the correction, otherwise passing through thepredictor signal. An additive combiner may be implemented when the useris aware of the current output of the predictor before it passes throughthe combiner and the persistent WTA.

The persistent WTA select output of the combiner (a vector ofpriorities), frame by frame, which of the available tasks should beperformed. In some implementations, the persistent WTA may make suchselection based on the following rules:

If the maximum of the input priorities is above a certain threshold,switch to the corresponding task.

Otherwise select the same task that had been selected in the previousframe.

The threshold parameter may be tuned to make the system more or lessprone to switching. With a high threshold, the system may need verystrong sensory evidence to switch from the current task, and vice-versawith a low threshold.

If the predictor outputs values for each possible pair of tasks toswitch between, instead of just one value per task, the persistent WTAmay work the same but may consider only the values of the pairs whosefirst task is the current one (the task that was selected in theprevious frame).

Apparatus and methods for using cost of user feedback during training ofrobots are disclosed herein, in accordance with one or moreimplementations. According to exemplary implementations, a user may wantto know about robot's performance without actually letting robot toperform the task autonomously. This may wholly or partially alleviateone or more disadvantages discussed above.

One starting point to solve this task may be to measure a current costfunction C of a predictor while its learning to do a task:

C(t)=d(y _(d)(t),y(t))  (Eqn. 1)

where C(t) represents current cost function at time t, y(t) representsoutput of the predictor (e.g., the component 422 of FIG. 4A), y_(d) (t)represents desired output of the predictor (signal from the teacher), drepresents distance function between desired and actual output (e.g.mean square error, Euclidean distance, and/or cross entropy).

The value of C(t) may be provided or shown to the user as a numberand/or in any other graphical form (e.g., progress bar, intensity of theLED, and/or other techniques for conveying a quantity). Based on thisnumber, the user may try to determine whether his corrections andpredictions of the system are close or not, which may indicate how wellor not the robot learned the task.

When a user shows the robot how to perform the task, he may do it indifferent ways on different occasions. For example, a user may teach therobot one or more possible obstacle avoidance trajectories which areclose to each other. The system may generalize those examples and choosea single trajectory. In some implementations, if the user gives a newexample of trajectory and measures costs according to Eqn. (1), thesystem may provide a large value indicating a mistake, even if onaverage the robot performs obstacle avoidance very well.

A possible solution may be to time-average (e.g., compute runningaverage or sliding average) the costs so that all occasional predictionerrors are not presented to the user. The user may receive a number thatrepresents how many mistakes a predictor did on average for a given timeinterval (e.g., 1 second, 5 minutes, and/or other time interval).

The numeric values of costs may depend on one or more factors includingone or more of the task, the predictor, the robot, the distance functionin Eqn. (1), and/or other factors. For example, if a robot is trained tofollow a trajectory with a constant linear velocity, then the costs maybe include costs of predicting angular velocity (e.g., costs on linearvelocity may be small because it may be easy to predict a constantvalue). However, if a task is Obstacle avoidance with backing up fromobstacles, then predicting of linear velocity may contribute the costs.Different predictors may achieve different costs on different tasks. Ifa robot is trained with eight degrees of freedom, a range of costs maybe different than costs during training navigation with two degrees offreedom (e.g., a (v, w) control space). Mean square error distancefunction used in Eqn. (1) may provide costs in different rangescomparing to cross entropy distance function.

In some implementations, in order to present costs to the user, it maybe useful to normalize them to interval (0, 1) by the maximum achievablecosts in this task (or by some fixed number if maximum costs areinfinite like in cross entropy case). Normalizing may provide moreindependence of the cost value to the distance function and robot.Normalized costs may depend on the task and on the predictor. However,numbers from (0, 1) may be readily represented to the user and comparedagainst each other.

Some tasks may differ from others in complexity and/or in statisticalproperties of a teacher signal. For example, compare a task A:navigating through a “right angle” path which consists of a straightline and then sudden turn and then straight line again and a task B:navigating a figure 8 path. In task A, costs may be really small even ifa prediction procedure always tells robot to drive straight withoutturning because costs of not turning are too small comparing to costs ofnot driving straight. A figure 8 path is more complex compared to theright angle path because the robot has to turn left and right dependingon the context. If a value of costs is provided to the user in the casesof the right angle path and the figure 8, the same values of the costsmay mean totally different performances on the actual task (small costson “right angle” may not mean a good performance, while small costs onFIG. 8 path may mean that the robot performs well).

To decrease sensitivity to the variations in the complexity and otherproperties of the task, a relative may be introduced to “blind”performance measure p_(b). A “blind” predictor may be used that does nottakes into account input of the robot and only predicts average valuesof control signal. It may compute a running (or sliding) average ofcontrol signal. In some implementations, a “blind” performance measurep_(b) may be expressed as:

p _(b)(t)=1−C(t)/C _(b)(t)  (Eqn. 2)

where C(t) represents costs computed using Eqn. (1) for a mainpredictor, C_(b)(t) represents costs computed using Eqn. (1) for a“blind” predictor. In some implementations, if p_(b)(t) is close to 1,then the prediction process may perform better than a baseline cost ofthe “blind” predictor. If p_(b)(t) is negative, then the main predictormay perform worse than a baseline.

In the example of training a “right angle” path, a blind predictor mayprovide low costs and be able to better perform the task the mainpredictor has to perform (which in this case means to perform also aturn and not only go straight). For a figure 8 path, a blind predictormay provide a high cost because it is not possible to predict when toswitch between left and right turns without input, so relativeperformance of the main predictor may be large even for relatively highcosts values.

A problem of presenting the costs to the user may be that costs maychange in time in highly non-linear fashion:

The user may prefer presentation of costs as decreasing in a linearfashion (e.g., a feedback number slowly decreases from 0 to 1 during thetraining). Otherwise a user may see a huge progress during suddendecrease of the costs function and then there will be almost no progressat all.

The general shape of the costs curve may be universal (or nearly so)among tasks and predictors. A reference predictor may be selected, whichis trained in parallel to the main predictor (i.e., the predictor thatthe robot actually uses to perform actions). A relative performancenumber may be expressed as:

p _(r)(t)=1−C(t)/C _(r)(t)  (Eqn. 3)

where C(t represents costs computed using Eqn. (1) for a main predictor,C_(r)(t) represents costs computed using Eqn. (1) for a referencepredictor. If p_(r)(t) is close to 1, then the main predictor mayperform better than the reference predictor. If p_(r)(t) is negative,then the main predictor may perform worse than the reference.

A reference predictor may be selected such that it generally behavesworse than a main predictor but still follows the dynamics of costs ofthe main predictor (e.g., curves on FIG. 1 should be close for referenceand for the main predictor). In some implementations, a single layerperceptron with sigmoidal outputs and mean square error distancefunction may be included in a good reference predictor. Linearity of asingle layer may be included in some predictor process, and may besufficient to achieve some performance on range of the tasks such asnavigation, manipulation, fetch, and/or other tasks where it exhibitsbehavior of costs depicted in FIG. 3A. An example of relativeperformance with this reference predictor is shown on FIG. 3B.

If there is noise in the teacher signal, noise in the environment,and/or the robot has changed, costs may increase because the mainpredictor has not yet adapted accordingly. However, if relative costsare used, this effect of noise (or robot change) may be diminishedbecause costs of reference predictor may also increase, but relativeperformance may not change significantly.

Different predictors may perform differently with different tasks.Sometimes a user may try different predictors on the same task todetermine which predictor is better for that task. Sometimes a user maytrain a robot to do different tasks using the same predictor. Todisentangle variations in the predictors from variations in the tasks, arelative performance number may be introduced that is independent of themain predictor p_(rb):

p _(rb)(t)=1−C _(r)(t)/C _(b)(t)  (Eqn. 4)

where C_(b)(t represents costs computed using Eqn. (1) for a “blind”predictor, C_(r)(t) represents costs computed using Eqn. (1) for areference predictor.

The main predictor p_(rb) may not depend on the main predictor the userchose to perform a task. If the reference predictor is fixed, p_(rb) maybe used to characterize the task complexity. Consider a case whenreference predictor is a linear perceptron. If p_(rb)is close to 1, thenthe task may be non-trivial so that the blind predictor cannot learn it,but simple enough for the linear predictor to learn it. If p_(rb) isclose to zero, then either task may be too complex for the linearpredictor to learn or it is trivial so that blind predictor achieves agood performance on it.

In some situations, it may be important to show to the user thatsomething in the training process went wrong (e.g., changes in theenvironment such as lighting conditions and/or other environmentalconditions, the user changing a training protocol without realizing it,and/or other ways in which the training process can be compromised). Toachieve that, changes may be detected in the flow of relativeperformance values (p_(rb)(t), p_(r)(t), p_(b)(t)) using step detectionalgorithms. For example, a sliding average of p(t) may be determined andsubtracted from the current value, and then normalized using eitherdivision by some max value or by passing into sigmoid function. Thevalue may be presented to the user. An average of steps for differentperformance values may be determined and presented to the user. If thevalue is large, then something may have gone wrong, according to someimplementations. For example, with p_(rb)(t), if the environment changedbut task is the same, then performance of the “blind” predictor may staythe same because it may be unaffected by task changes, but performanceof reference predictor may drop.

In the case of using several reference predictors [p0 . . . pn] that aretrained in parallel to the main one, performance numbers may bedetermined from any pair of them:

p _(ij)(t)=1−C _(i)(t)/C _(j)(t)  (Eqn. 5)

where C_(i)(t) represents costs computed using Eqn. (1) for a i-threference predictor, C_(i)(t) represents costs computed using Eqn. (1)for a j-th reference predictor,

Depending on the properties of those reference predictors, performancenumbers may characterize task, main predictor, and/or the whole trainingprocess differently. For example, [p0 . . . ,pn] may include a sequenceof predictors so that a subsequent predictor is more “powerful” than aprevious one (e.g., “blind”, linear, quadratic, look up table). The setof performance numbers may characterize how difficult is the task (e.g.,only look up table predictor gets a good score vs. a task where linearpredictor is already doing fine).

Reference predictors [p0 . . . pn] may include a sequence of predictorssimilar to the main predictor but with different parameters (e.g.learning coefficient). Performance numbers may be indicative of hownoisy is the teacher signals and/or environment. In someimplementations, if there a lot of noise, only predictors with a smalllearning coefficient may be able to learn the task. If training signalsand features are clean (i.e., low or no noise), then a predictor withhigh learning coefficient may be able to learn the task.

A matrix of reference numbers p_(ij)(t) for a given set of predictors[p0 . . . pn] for different tasks may be provided into a clusteringalgorithm, which may uncover clusters of similar tasks. After thatduring training a new task, this clustering algorithm may provide to theuser a feedback that the current task is similar in properties to thetask already seen (e.g., so that the user can make a decision on whichtraining policy to pick).

Predictor apparatus and methods are disclosed herein, in accordance withone or more implementations. FIG. 4A illustrates an implementation ofadaptive control system 400. The adaptive control system 400 of FIG. 4Amay comprise a corrector 412, an adaptive predictor 422, and a combiner414 cooperating to control a robotic platform 430. The learning processof the adaptive predictor 422 may comprise a supervised learningprocess, a reinforcement learning process, and/or a combination thereof.The corrector 412, the predictor 422 and the combiner 414 may cooperateto produce a control signal 420 for the robotic platform 410. In one ormore implementations, the control signal 420 may comprise one or moremotor commands (e.g., pan camera to the right, turn right wheelforward), sensor acquisition commands (e.g., use high resolution cameramode), and/or other commands.

In some implementations, the predictor 422 and the combiner 414components may be configured to operate a plurality of roboticplatforms. The control signal 420 may be adapted by a decoder component424 in accordance with a specific implementation of a given platform430. In one or more implementations of robotic vehicle control, theadaptation by the decoder 424 may comprise translating binary signalrepresentation 420 into one or more formats (e.g., pulse codemodulation) that may be utilized by given robotic vehicle. U.S. patentapplication Ser. No. 14/244,890 entitled “LEARNING APPARATUS AND METHODSFOR CONTROL OF ROBOTIC DEVICES”, filed Apr. 3, 2014 describes someimplementations of control signal conversion.

In some implementations of the decoder 424 corresponding to the analogcontrol and/or analog corrector 412 implementations, the decoder may befurther configured to rescale the drive and/or steering signals to arange appropriate for the motors and/or actuators of the platform 430.

In some implementations of the discrete state space controlimplementation of the corrector 412, the decoder 424 may be configuredto convert an integer control index into a corresponding steering/drivecommand using, e.g. a look up table approach described in detail in,U.S. patent application Ser. No. 14/265,113 entitled “TRAINABLECONVOLUTIONAL NETWORK APPARATUS AND METHODS FOR. OPERATING A ROBOTICVEHICLE”, filed Apr. 29, 2014, the foregoing being incorporated hereinby reference in its entirety.

The corrector 412 may receive a control input 428 from a control entity.The control input 428 may be determined based on one or more of (0sensory input 402 and (ii) feedback from the platform (not shown). Insome implementations, the feedback may comprise proprioceptive signals,such as feedback from servo motors, joint position sensors, and/ortorque resistance. In some implementations, the sensory input 402 maycorrespond to the sensory input, described, e.g., with respect to FIG.1, supra. In one or more implementations, the control entity providingthe input 428 to the corrector may comprise a human trainer,communicating with the robot via a remote controller (wired and/orwireless). In some implementations, the control entity may comprise acomputerized agent such as a multifunction adaptive controller operableusing reinforcement and/or unsupervised learning and capable of trainingother robotic devices for one and/or multiple tasks. In one suchimplementation, the control entity and the corrector 412 may comprise asingle computerized apparatus.

The corrector 412 may be operable to generate control signal 408 using aplurality of approaches, in some implementations of analog control forrobotic vehicle navigation, the corrector output 408 may comprise targetvehicle velocity and target vehicle steering angle. Such implementationsmay comprise an “override” functionality configured to cause the roboticplatform 430 to execute action in accordance with the user-providedcontrol signal instead of the predicted control signal.

In one or more implementations of analog correction provision forrobotic vehicle navigation, the control signal 408 may comprise acorrection to the target trajectory. The signals 408 may comprise atarget “correction” to the current velocity and/or steering angle of theplatform 430. In one such implementation, when the corrector output 408comprises a zero signal (or substantially a null value), the platform430 may continue its operation unaffected.

In some implementations of state space for vehicle navigation, theactions of the platform 430 may be encoded using, e.g., a 1-of-10integer signal, where eight (8) states indicate 8 possible directions ofmotion (e.g., forward-left, forward, forward-right, left, right,back-left, back, back-right), one state indicates “stay-still”, and onestate indicates “neutral”. The neutral state may comprise a defaultstate. When the corrector outputs a neutral state, the predictor maycontrol the robot directly. It will be appreciated by those skilled inthe arts that various other encoding approaches may be utilized inaccordance with controlled configuration of the platform (e.g.,controllable degrees of freedom).

In some implementations of control for a vehicle navigation, the actionspace of the platform 430 may be represented as a 9-element statevector, e.g., as described in, e.g., the above referenced U.S. patentapplication '113. Individual elements of the state vector may indicatethe probability of the platform being subjected to (i.e., controlledwithin) a given control state. In one such implementation, output 418 ofthe predictor 422 may be multiplied with the output 408 of the corrector412 in order to determine probability of a given control state.

The adaptive predictor 422 may be configured to generate predictedcontrol signal u^(p) 418 based on one or more of (i) the sensory input402 and the platform feedback (not shown). The predictor 422 may beconfigured to adapt its internal parameters, e.g., according to asupervised learning rule, and/or other machine learning rules.

Predictor realizations comprising platform feedback, may be employed inapplications such as, for example, where: (i) the control action maycomprise a sequence of purposefully timed commands (e.g., associatedwith approaching a stationary target (e.g., a cup) by a roboticmanipulator arm), or where (ii) the platform may be characterized byplatform state parameters (e.g., arm inertia, and/or motor responsetime) that change faster than the rate of action updates. Parameters ofa subsequent command within the sequence may depend on the control plantstate; a “control plant” refers to the logical combination of theprocess being controlled and the actuator (often expressedmathematically). For example, control plant feedback might be the exactlocation and/or position of the arm joints which can be provided to thepredictor.

In some implementations, the predictor 422 may comprise a convolutionalnetwork configured to predict the output 420 of the combiner 414 giventhe input 402. The convolutional network may be combined with othercomponents that learn to predict the corrector signal given otherelements of the sensory context. When the corrector 412 output comprisesa zero signal (or null value), the combiner output 420 may equal thepredictor output 418. When the corrector provides a non-zero signal, adiscrepancy may occur between the prediction 418 and the output 420 ofthe combiner 414. The discrepancy may be utilized by the predictor 422in order to adjust parameters of the learning process in order tominimize future discrepancies during subsequent iterations.

The sensory input and/or the plant feedback may collectively be referredto as sensory context. The sensory context may be utilized by thepredictor 422 to produce the predicted output 418. By way of anon-limiting illustration, one exemplary scenario of obstacle avoidanceby an autonomous rover uses an image of an obstacle (e.g., wallrepresentation in the sensory input 402) combined with rover motion(e.g., speed and/or direction) to generate Context_A. When the Context_Ais encountered, the control output 420 may comprise one or more commandsconfigured to avoid a collision between the rover and the obstacle.Based on one or more prior encounters of the Context_A—avoidance controloutput, the predictor may build an association between these events asdescribed in detail below.

The combiner 414 may implement a transfer function h(v) where x includesthe control signal 408 and the predicted control signal 418. In someimplementations, the combiner 414 operation may be expressed, e.g., asdescribed in detail in co-owned U.S. patent application Ser. No.13/842,530 entitled “ADAPTIVE PREDICTOR APPARATUS AND METHODS”, filedMar. 15, 2013, as follows:

û=h(u,u ^(p)).  (Eqn. 6)

Various realizations of the transfer function of Eqn. 6 may be utilized.In some implementations, the transfer function may comprise one or moreof: addition, multiplication, union, a logical ‘AND’ operation, alogical ‘OR’ operation, and/or other operations.

In one or more implementations, the transfer function may comprise aconvolution operation, e.g., a dot product. In spiking networkrealizations of the combiner function, the convolution operation may besupplemented by use of a finite support kernel (i.e., a mapping functionfor linear space to a non-linear space) such as Gaussian, rectangular,exponential, etc. In one embodiment, a finite support kernel mayimplement a low pass filtering operation of input spike train(s). Insome implementations, the transfer function h may be characterized by acommutative property. (Eqn. 7)

In one or more implementations, the transfer function of the combiner414 may be configured as follows:

h(0,u ^(p))=u ^(p).  (Eqn. 8)

In some implementations, the transfer function h may be configured as:

h(u,0)=u.  (Eqn. 9)

In some implementations, the transfer function h may be configured as acombination of realizations of Eqn. 8-Eqn. 9 as:

h(0,u ^(p))=u ^(p), and h(u,0)=u,  (Eqn. 10)

In one exemplary implementation, the transfer function satisfying Eqn.10 may be expressed as:

h(u,u ^(p))=(1−u)×(1−u ^(p))−1  (Eqn. 11)

In one such realization, the combiner transfer function is configuredaccording to Eqn. 8-Eqn. 11, to implement additive feedback. In otherwords, output of the predictor (e.g., 418) may be additively combinedwith the control signal (408) and the combined signal 420 may be used asthe teaching input (404) for the predictor. In some implementations, thecombined signal 420 may be utilized as an input (context) into thepredictor 422, e.g., as described in co-owned U.S. patent applicationSer. No. 13/842,530 entitled “ADAPTIVE PREDICTOR APPARATUS AND METHODS”,filed Mar. 15, 2013, incorporated supra.

In some implementations, the combiner transfer function may becharacterized by a delay expressed as:

{circumflex over (u)}(t _(i+1))=h(u(t _(i)),u ^(p)(t _(i))),  (Eqn. 12)

where û(t_(i+1)) denotes combined output (e.g., 420 in FIG. 4A) at timet+Δt.

As used herein, symbol t_(i) may be used to refer to a time instanceassociated with individual controller update events (e.g., as expressedby Eqn. 12), for example t₁ denoting time of the first control output,e.g., a simulation time step and/or a sensory input frame step. In someimplementations of training autonomous robotic devices (e.g., rovers,bi-pedaling robots, wheeled vehicles, aerial drones, robotic limbs,and/or other robotic devices), the update periodicity Δt may beconfigured to be between 1 ms and 1000 ms.

In some implementations, the combiner transfer function may beconfigured to implement override functionality (e.g., overridecombiner). The “override” combiner may detect a non-zero signal providedby the corrector, and provide a corrector signal as the combined output.When a zero (or no) corrector signal is detected, the predicted signalmay be routed by the combiner as the output. In some implementations,the zero corrector signal may be selected as not a value (NaN); thenon-zero signal may comprise a signal rather than the NaN.

In one or more implementations of a multi-channel controller, thecorrector may simultaneously provide “no” signal on some channels and“a” signal on others, allowing the user to control one degree of freedom(DOF) of the robotic platform while the predictor may control anotherDOE

It will be appreciated by those skilled in the art that various otherrealizations of the transfer function of the combiner 414 may beapplicable (e.g., comprising a Heaviside step function, a sigmoidfunction, such as the hyperbolic tangent, Gauss error function, logisticfunction, and/or a stochastic operation). Operation of the predictor 422learning process may be aided by a teaching signal 404. As shown in FIG.4A, the teaching signal 404 may comprise the output 420 of the combiner414. In some implementations wherein the combiner transfer function maybe characterized by a delay (e.g., Eqn. 12), the teaching signal at timet_(i) may be configured based on values of u, u^(p) at a prior timet¹⁻¹, for example as:

u ^(d)(t _(i))=h(u(t _(i−1)),u ^(p)(t _(i−1))),  (Eqn. 13)

The training signal u^(d) at time t_(i) may be utilized by the predictorin order to determine the predicted output u^(p) at a subsequent timet_(i+1), corresponding to the context (e.g., the sensory input x) attime t_(i);

u ^(p)(t _(i+1))=F[x _(i) ,W(u _(d)(t _(i)))].  (Eqn. 14)

In Eqn. 14, the function W may refer to a learning process implementedby the predictor, e.g., a perceptron, and/or a look-up table.

In one or more implementations, such as illustrated in FIG. 4A, thesensory input 406, the control signal 408, the predicted output 418, thecombined output 420 and/or plant feedback may comprise spiking signals,analog signals, and/or a combination thereof. Analog to spiking and/orspiking to analog signal conversion may be effectuated using, mixedsignal spiking neuron networks, such as, for example, described inco-owned U.S. patent application Ser. No. 13/313,826 entitled “APPARATUSAND METHODS FOR IMPLEMENTING LEARNING FOR ANALOG AND SPIKING SIGNALS INARTIFICIAL NEURAL NETWORKS”, filed Dec. 7, 2011, and/or co-owned U.S.patent application Ser. No. 13/761,090 entitled “APPARATUS AND METHODSFOR IMPLEMENTING LEARNING FOR ANALOG AND SPIKING SIGNALS IN ARTIFICIALNEURAL NETWORKS”, filed Feb. 6, 2013, incorporated supra.

Output 420 of the combiner e.g., 414 in FIG. 4A may be gated. In someimplementations, the gating information may be provided to the combinerby the corrector 412 using, e.g., an “override” indication in order tocause the robotic platform 430 to execute actions according to theuser-provided control instead of the predicted control signal.

En one such realization of spiking controller output, the control signal408 may comprise positive spikes indicative of a control command andconfigured to be combined with the predicted control signal (e.g., 418);the control signal 408 may comprise negative spikes, where the timing ofthe negative spikes is configured to communicate the control command,and the (negative) amplitude sign is configured to communicate thecombination inhibition information to the combiner 414 so as to enablethe combiner to ‘ignore’ the predicted control signal 418 forconstructing the combined output 420.

In some implementations of spiking signal output, the combiner 414 maycomprise a spiking neuron network; and the control signal 408 may becommunicated via two or more connections. One such connection may beconfigured to communicate spikes indicative of a control command to thecombiner neuron; the other connection may be used to communicate aninhibitory signal to the combiner network. The inhibitory signal mayinhibit one or more neurons of the combiner the one or more combinerinput neurons of the combiner network thereby effectively removing thepredicted control signal from the combined output (e.g., 420 in FIG. 4).

The gating information may be provided to the combiner by another entity(e.g., a human operator controlling the system with a remote controland/or external controller) and/or from another output from thecorrector 412 (e.g., an adaptation block, an optimization controller).In one or more implementations, the gating information may comprise oneor more of: a command, a memory address of a register storing a flag, amessage, an inhibitory efficacy, a value (e.g., a weight of zero to beapplied to the predicted control signal by the combiner), and/or otherinformation capable of conveying gating instructions to the combiner.

The gating information may be used by the combiner network to inhibitand/or suppress the transfer function operation. The suppression (or‘veto’) may cause the combiner output (e.g., 420) to be comprised solelyof the control signal portion 418, e.g., configured in accordance withEqn. 9. In one or more implementations the gating information may beused to suppress (‘veto’) provision of the context signal to thepredictor without affecting the combiner output 420. In one or moreimplementations the gating information may be used to suppress (‘veto’)the feedback from the platform.

In one or more implementations, the gating signal may comprise aninhibitory indication that may be configured to inhibit the output fromthe combiner. Zero combiner output may, in some realizations, may causezero teaching signal (e.g., 414 in FIG. 4A) to be provided to thepredictor so as to signal to the predictor a discrepancy between thetarget action (e.g., controller output 408) and the predicted controlsignal (e.g., output 418).

The gating signal may be used to veto predictor output 418 based on, forexample, the predicted control output 418 being away from the targetoutput by more than a given margin. The margin may be configured basedon an application and/or state of the trajectory. For example, a smallermargin may be applicable in navigation applications wherein the platformis proximate to a hazard (e.g., a cliff) and/or an obstacle. A largererror may be tolerated when approaching one (of many) targets.

In one or more implementations, the gating/veto functionality may beimplemented on a “per-channel” basis in a multi-channel controllerwherein some components of the combined control vector may comprisepredicted components, while some components may comprise the correctorcomponents.

By way of a non-limiting illustration, if the turn is to be completedand/or aborted (due to, for example, a trajectory change and/or sensoryinput change), and the predictor output still produces turn instructionsto the plant, the gating signal may cause the combiner to veto (ignore)the predictor contribution and pass through the controller contribution.

Predicted control signal 418 and the control input 408 may be ofopposite signs. In one or more implementations, a positive predictedcontrol signal (e.g., 418) may exceed the target output that may beappropriate for performance of as task. The control signal 408 may beconfigured to include negative signaling in order to compensate forover-prediction by the predictor.

Gating and/or sign reversal of controller outputs may be useful, forexample, where the predictor output is incompatible with the sensoryinput (e.g., navigating towards a wrong target). Rapid changes in theenvironment (compared to the predictor learning time scale caused bye.g., appearance of a new obstacle, target disappearance), may requirean “override” capability for the controller (and/or supervisor) to‘override’ predictor output. In one or more implementations compensationfor over-prediction may be controlled by a graded form of the gatingsignal.

In some implementations, the predictor learning process may beconfigured based on one or more look-up tables (LUT). Table 1 and Table2 illustrate the use of look up tables for learning obstacle avoidancebehavior.

Table 1 and Table 2 present exemplary LUT realizations characterizingthe relationship between sensory input (e.g., distance to obstacle d)and control signal (e.g., turn angle α relative to current course)obtained by the predictor during training. Columns labeled N in Table 1and Table 2, present use occurrence N (i.e., how many times a givencontrol action has been selected for a given input, e.g., distance).Responsive to the selection of a given control action (e.g., turn of15°) based on the sensory input (e.g., distance from an obstacle of 0.7m), the counter N for that action may be incremented. In someimplementations of learning comprising opposing control actions (e.g.,right and left turns shown by rows 3-4 in Table 2), responsive to theselection of one action (e.g., turn of +15°) during learning, thecounter N for that action may be incremented while the counter for theopposing action may be decremented.

As seen from the example shown in Table 1, the controller may produce aturn command as a function of the distance to Obstacle falling to agiven level (e.g., 0.7 in). As shown, a 15° turn is most frequentlyselected during the training for sequence. In some implementations, thepredictor may be configured to store the LUT (e.g., Table 1) data foruse during subsequent operation. During operation, the most frequentlyused response (e.g., turn of 15°) may be output for a given sensoryinput. In some implementations, the predictor may output an average ofstored responses (e.g., an average of rows 3-5 in Table 1).

TABLE 1 d α° N 0.9 0 10 0.8 0 10 0.7 15 12 0.7 10 4 0.7 5 1 . . . 0.5 453

TABLE 2 d α° N 0.9 0 10 0.8 0 10 0.7 15 12 0.7 −15 4 . . . 0.5 45 3

In some implementations, the predictor 422 learning process may beconfigured to detect targets and/or obstacles based on sensory input(e.g., 402 in FIG. 2). In some implementations, the detection may beconfigured based on an operation of a multi-layer perceptron and/or aconvolutional network.

Training apparatus and methods are disclosed herein, in accordance withone or more implementation. Exemplary implementations may facilitateidentifying multiple solutions (also referred to herein as teachingmode) that have a value when training a robot. Depending on one or moreof the type of robot, the task, the state of training, and/or otherinformation, the teacher may switch from one teaching mode to anotherone to teach a behavior in the most effective manner.

In some implementations, the control signal may include a combination ofa correction signal and a prediction signal. The correction signal maybe given by a teacher (e.g., a human user controlling the robot and/oran algorithm mastering the task). The prediction signal may be learnedwhile performing the task by a module called Predictor. The combinationof the two signals may be performed by the combiner (e.g., ModeCombinerin the diagram below).

FIG. 4B illustrates an adaptive controller apparatus comprising a modecombiner for use with, e.g., the robotic apparatus of FIG. 1, accordingto one or more implementations.

There may be multiple behaviors the robot can perform when the teachersends a correction signal. Examples of those behaviors may include oneor more of:

-   -   Execute the correction and learn the association between the        context and the correction;    -   Execute the correction but do not learn the association;    -   Integrate both the correction and the prediction (e.g., by        adding them) and execute the resulting command;    -   Ignore the correction and execute the prediction; and/or    -   Other behaviors.

There may be one or more ways the robot can behave when the teacher isnot sending any correction. Examples of those behaviors may include oneor more of:

-   -   Execute the association it learned in the past (the prediction);    -   Don't do anything; and/or    -   Other behaviors.

Some implementations may provide five different modes that use acombination of what the robot does whether the teacher sends acorrection or not. Those five combinations may assist teaching abehavior in the most effective manner.

In some implementations, the available modes may include one or more ofControl, Listen, Override, Correct, Autonomous, and/or other modes.Exemplary implementations of various modes are described in the tablebelow.

TABLE 3 Action of the robot in presence of training Action of the robotin Mode input absence of training input Control Executes the action inaccordance with the Idle training input; Does not learn the associationListen Executes the action in accordance with the Idle training input;Learns the association Override Executes the action in accordance withthe Executes the action in training input; Learns the associationaccordance with the prediction Correct Combine the teaching input andthe prediction Executes the action in and execute the action unaccordance with the accordance with the combined signal. Learn theresulting association prediction Autonomous Ignore the teaching inputand execute the action Executes the action in in accordance with theprediction accordance with the prediction

In some implementations, the available modes may be embodied in and/oreffectuated by the combiner (also referred to herein as ModeCombiner).

The combiner mode may be changed either by the teacher (e.g., the humanteaching the robot), and/or by an internal mechanism that determines thestate the combiner should be in based on the internal of the system.

According to some implementations, the teacher may switch from oneteaching mode to another one using the iPhone App, as depicted in thefigure below.

FIG. 18 illustrates an exemplary correction screen with Listen modeactivated, and Override Correct and autonomous mode available from theteacher control screen, in accordance with one or more implementations.

In control mode, the robot may be remote controlled. Responsive to theteacher sending a correction, the robot may execute the command but maynot learn the association. If the teacher is not sending any correction,then the robot may stay still. This mode may be useful when the teacherwants to control the robot without teaching (e.g., if the teacher isrepositioning the robot to a starting position, but the teacher does notwant the robot to do that on its own).

In listen mode, the robot may be “listening” to or otherwise observingwhat the teacher teaches, and may not do anything on its own. However,the robot may learn an association. But if the teacher stops sending acommand, the robot may stay still and wait for the next command. In someimplementations, teaching the robot may begin with the listen mode. Onceenough examples have been provided and the robot has learned something,the override mode may be used.

In the override mode, the robot may execute what it has learned, unlessa command is sent by the teacher. As soon as the teacher starts sendingcommands, the robot may stop taking the initiative and may let theteacher control it. For example, if the robot is turning left but theteacher wants the robot to turn right and provides a right turn command,then the robot may head the teacher's command, perform the action, andtry to remember it for the next time the same situation occurs. Once abehavior only needs fine tuning, the correct mode may be used.

In the correct mode, the robot may integrate what the teacher commandswith what the robot already knows. In some implementations, the robotmay sum the teacher's command with what the robot already knows to get afinal motor command. The teacher's correction may operate in this caseas a deviation from the predicted command determined by the robot.

By way of non-limiting illustration, the robot may be driving full speedin a course. The teacher may want to teach the robot not to go so fast.A natural reaction for the teacher might be to press a “go-back button”on a gamepad used to provide commands to the robot. If the teacher doesthat in the override mode, which may tell the robot to drive backward,not to decrease its speed (the teacher still wants the robot to moveforward in this context). The correct mode may be appropriate for thissituation. The robots might say, “I like this blue trash bin over there,I am driving there as fast as I can,” and the teacher may say, “Heychamp, you are going a little bit too fast, I would suggest that youreduce your speed.” Both variables may be added or otherwise combined,and at the end the robot might think something like, “Well, I still likethis bin, but maybe I should go there a little bit more carefully.”

The autonomous mode may provide a way for the teacher to send acorrection to the robot. In this mode, the learned behavior may beexpressed without any changes or learning.

FIG. 19 illustrates one set of transition between teaching modes whilelearning a new task, in accordance with one or more implementations.

At operation 1902, the module may operate in the CONTROL mode. Theteacher may tele-operate the robot and position it in a desired state.

At operation 1904, the teacher may switch to the LISTEN mode to initiatelearning. The teacher may show a few examples of the task to the robot,but may not want the robot to interfere with the teacher's teachingduring this process.

At operation 1906, after a stage of training, the teacher may switch tothe OVERRIDE mode. The teacher may let the robot operate autonomouslywhile retaining capability of providing correction(s) when the robot isnot expressing the target behavior.

At operation 1908, the teacher may switch to the CORRECT mode. In thismode, the teacher may only provide small corrections delta corrections)to optimize the behavior.

At operation 1910, once the teacher may determine that the behavior hasbeen learned by the robot with a sufficient accuracy (e.g., based on anerror determined with a based on a comparison of a target actionperformance and actual action performance), the teacher may switch therobot to the AUTONOMOUS mode, which may prevent any intervention fromthe teacher, and also provide a validation mode to test performancelevel.

In some implementations, switching from one mode to another may be donemanually by the teacher (e.g., through a Smartphone App and/or othercontrol mechanism). In some implementations, switching between modes maybe based on an internal variable representing the state system (e.g.,time, number of correction given, amplitude of the last n-corrections,quality of predictions, and/or other information).

Apparatus and methods for hierarchical learning are disclosed herein, inaccordance with one or more implementations. In supervised learning, auser may train simple tasks by demonstrating example tasks to thesystem. To achieve an acceptable performance of a complex task by thesystem, the user may provide additional information to the system, inaddition to examples. In particular, with the system, the user mayorganize simple “low level” behaviors into hierarchies and train extra“high level” classifiers or “switchers” to control which low levelbehaviors should be active in a given context. In some implementations,the user may select which particular information will be considered asrelevant for a particular task. The user may select which particularprediction method will be used for a particular behavior.

For example, a task of playing fetch may be quite complex: the robot maybe supposed to find an object, grasp it, and bring it back to base. Inresponse to the robot not “seeing” the target, the robot may perform arandom exploration behavior avoiding obstacles. In response to the robotbeing close to the target, the robot may perform a positioning maneuverand grasp the target. If the target is in the robot's gripper, the robotmay find its way to the base avoiding obstacles and then release theobject. The whole task may be highly nonlinear and/or noisy, so a verypowerful classifier may infer the right sequence of actions from justthe examples and be resistant to noise. The classifier may need todetermine which particular input is important for a particular task. Forexample, a state of the gripper may not be relevant to target searchbehavior. Additional training may be needed to show the predictor thatthe search behavior should be performed independently of the state ofthe gripper.

According to exemplary implementations, the user may train a componentof the fetch behavior. For example, the user may train how to search fora target and/or how to approach it while avoiding obstacles (e.g.,“target approach”). The user may train the robot to perform a graspingbehavior in various contexts (e.g., “grasping a target”). The user mayassume the robot successfully grasped the target and train it to findits way to the base (“base approach”). While training a particularbehavior, the user may concentrate on a particular behavior and may notshow any examples of other behaviors thus making a prediction task forthe classifier substantially easier (e.g., less computationallyintensive). The user may have an opportunity to select which input isrelevant for a particular behavior. For example, in someimplementations, only the target and obstacle detection may be relevantto train a target approach, and/or only base and obstacle detection maybe relevant to train a base approach. The user may select whichactuators are controlled by a particular behavior. For example,navigation tasks may not need to learn to control the gripper. The usermay have an opportunity to use a specialized predictor for different lowlevel tasks. For example, a non-linear classifier may be used for targetapproach and a linear classifier may be used for grasping.

After achieving a reasonable performance in individual tasks, the usermay create a hierarchy from these behaviors. The figure below shows oneexemplary hierarchy, in accordance with one or more implementations.

According to the hierarchy depicted in FIG. 9, the “Fetch switcher” maybe a high level behavior that activates one or more low level behavior.Such activation may depend on the context. The user may train the “Fetchswitcher” using supervised learning. For example, the user maydemonstrate to the switcher which behavior it should select in aparticular situation. The user may use a user interface for suchdemonstrations, according to some implementations. In someimplementations, if the target is far or not visible, the user may senda “select target approach” command. If the target is close, the user maysend a “select grasping a target” command A classifier included in theswitcher may learn to predict which module to select depending on visualcontext. The user may select which input is important for switching(e.g., obstacle detection may not be necessary in some situations).

Exemplary implementations of the system may support hierarchy graphs ofdifferent complexity. The following figure illustrates one example of agraph including one module, in accordance with one or moreimplementations.

The system may be configured to learn from user commands to control theenvironment (e.g. robot) by paying attention to features provided by thefirst and the last feature extractor. For example, the user may controlthe robot using a gamepad (or other input device used to provide “usercommands”) to approach the target while avoiding the obstacles(“Behavior 1”) using input from target tracker (“FE 1”) and fromobstacle detector (“FE 1”). After a while “Adaptive controller” of type“1” (e.g., kNN classifier) may learn to approach the target autonomouslygenerating correction torques on robot's wheels (“output1” and“output2”) and may not control the gripper (“output N”), “Sensory input”may include input from the cameras and encoders.

Referring still to the figure above, the module may receive anactivation signal. The activation signal may indicate whether a moduleshould be active. In some implementations, the activation signal mayinclude or convey a binary number where “0” means the module is notactive and “1” means that module is active. If the module is active(highlighted green on the figure), then the module may process inputfeatures and/or teacher commands, and may provide an output. Anon-active module may not by executed, which may save computational timeon other operations.

The user may have an ability to perform managing operations on “Behavior1”. For example, the user may perform operations including one or moreof change a name of a module, reset a module to naive state, revert amodule to a particular point in history, delete a module, save/loadlocally, upload, download, and/or other operations. The user may changeoutputs and FE1 Such changes may depend on whether it is compatible witha trained behavior (e.g., dimensionality of features and outputs ispreserved), in some implementations. The user may train a targetapproach. The user may change input from a target tracker to a basetracker so that robot will approach to base. A robot with multiplegrippers may be trained to grasp with gripper 1 and user wired outputfrom the adaptive module form gripper 1 to gripper 2.

After is user is done training a particular behavior, he may createanother module and activate it. Such creation is depicted in the figurebelow, in accordance with one or more implementations.

The system may be configured to learn the second behavior from the user.Figure above shows that the user may select a different set of inputfeatures and a different set of outputs for “Behavior 2”. The user mayselect a different type of the adaptive controller (“2”). For example,the user may control the robot using a gamepad (or other input deviceused to provide “user commands”) to turn and grasp the target (“Behavior2”) using input from target tracker (“FE 1”) and state of the gripper(“FE 2”). “Adaptive controller” of type “2” (e.g., perceptronclassifier) may learn to turn the robot appropriately and grasp thetarget by autonomously effectuating correct torques on the robot's wheel(“output 2”) and gripper (“output N”), while not controlling anotherwheel (“output 1”), “Sensory input” may include input from the camerasand encoders.

The user may perform the same managing operation with “Behavior 2”. Theuser may manually (e.g., using a user interface) select which moduleshould be active at a given time by sending different activationsignals. “Behavior 2” may be active on the figure above (highlightedgreen) because it receives activation signal 1 and “Behavior 2” may notbe active because it receives activation signals 0. The user may decideto train a target approach more by activating behavior 1, training it,and activating behavior 2 again. The user may have good two behaviorsand uses manual switching (e.g., via a user interface) to give highlevel commands to the robot (e.g., “kick”, “punch”, “escape” in roboticfighting game).

After user is done training a set of “low level” behaviors, he maycreate a hierarchy with a switcher module on top, in accordance withsome implementations. One example of such a hierarchy is depicted in thefigure below.

The system may be configured to learn to switch between low levelbehaviors from the user's input. The figure above shows that the usermay select a different set of input features for the switcher and adifferent set of modules to switch between (all low level behaviors areselected on the figure above). The user may train the switcher to switchbetween “Target approach”, “Base approach”, and “Grasp the target” basedon features from one or more of the target tracker, the base tracker,the gripper state, and/or based on other information. The switcher mayuse different adaptive components (e.g., a stateful switch).

The output to the switcher may be wired to activation input ports of lowlevel behaviors. In some implementations, the output of the switcher maybe binary, so that switcher activates and/or deactivates particularmodules. Deactivated modules may not be executed to save computationalresources. In some implementations, the switcher may activate only asingle module at a time.

The user may perform the same or similar managing operation as theswitcher. The user may change which low level modules are controlled bythe switcher. For example, the user may train fetch with a “Baseapproach” behavior so the robot does not drop the object near the base.The user may change output of the switcher to use “Base approach withdropping object”. The user may customize complex behavior by changingwhich low-level modules are controlled by the switcher.

After the user is done training a single complex behavior, he may trainanother switcher to switch between different modules and thereforeobtain the second complex behavior available in the system. This isillustrated in the figure below, in accordance with one or moreimplementations.

The switchers may have a different set of low-level modules to switchbetween. The user may activate different high-level behaviors (e.g.using a user interface) depending on what is the objective. For example,the first high level behavior may be find an object and bring it to thebase, and the second high level behavior may be to take an object nearbase and bring it to the center of the room. In this case, several lowlevel behaviors may be reused (e.g., grasping). The user may switchbetween bringing an object to the base or to the center of the roomdepending on the time of the day.

After training high-level switchers, the user may train even more highlevel switcher on top and then go on as much as he like creatingarbitrary deep complex behaviors. This is illustrated in the figurebelow, in accordance with one or more implementations.

There may be no requirement that activations from level K should go tolevel K-1, in some implementations. It may be possible to sendactivations to any module in the system (e.g., from K level to level 1or 0) as long as the graph of activations does not have loops, accordingto some implementations.

If activations are binary signals, then at individual time steps theremay be an active path in the graph the leads from higher level to theoutputs. Modules in an active path may be executed, while other modulesmay be dormant.

Referring to the figure below, which illustrates a dog behavior, theuser may train fetch (e.g., two level behavior), stay close to a human(e.g., simple behavior), and bring object to center of the room. Theuser may train the third level switchers (e.g., one that switchesbetween stay close to the person if person is identified and do fetchotherwise). The switcher may bypass the second level to activate the“follow a person” behavior. Another, switch may switch between bringobject to the center of the room if it is dark outside and do fetchotherwise. The user may trains a fourth level switcher that switchesbetween do “fetch and close to human strategy” if robot is outside anddo “fetch and bring object to the center” if inside the house.

Individual modules may support an interface of getting features, humanteaching signal, and activation signal. Different modules may includedifferent logic.

Some implementations may include float activations (e.g., from 0 to 1)so that a module scales its output accordingly. This scheme may alloworganizing an adaptive weighted mixture of behaviors depending on thecontext.

In some implementations, e.g., such as described above in connectionwith FIGS. 4 and/or 10A-10B a random k-nearest neighbors (RKNN) approachmay be used for associating sensory context with one or motor actions.In some implementations, the RKNN methodology may comprise onlinelearning of predicting output y (e.g., motor commands) based on theinput (e.g., plurality of features detected in sensory input).

The RKNN process may utilize a plurality of sensory inputs in order topredict motor command for controlling operation of a robot, in someimplementations, the sensory input may comprise inputs characterized bydifferent degrees of redundancy. In some implementations, the redundancymay be characterized by number of degrees of freedom (e.g., independentstates) that may be conveyed by the input. By way of an illustration, abinary input (for example “ON”/“OFF”) indicative of wheel rotation (orlack thereof), proximity sensor output (ON, OFF), battery level belowthreshold, and/or other binary input may be characterized by lower levelof redundancy compared to other inputs (e.g., video, audio). In someimplementations of robotic vision based navigation, the input space maybe regarded as having high dimensionality and/or highly redundant,compared to other inputs (e.g., audio, touch). In one or moreimplementations, an input characterized by number of dimensions that mayat least 10 times that of be greater than number of dimensions ofanother input may be referred to as highly dimensional and/or highlyredundant, compared to the other input.

When a highly redundant input may be augmented with data of lowerredundancy, the highly redundant data may overwhelm the less redundantdata when determining response of a KNN classifier.

The RKNN process may partition available data into subsets comprising agiven number of features from the lower-dimension/lower redundancy data.The given number of features associated with lower-dimension/lowerredundancy data may be referred to as the mandatory feature(s). As usedherein the term feature may be used to describe one or more integer orfloating point values characterizing the input, e.g., the presence orabsence of an edge, corner, shape, texture, color, object, at particularlocations in the image, values of pixels in an image, patches of colortexture, brightness in the image, and/or in the image as a whole;properties of the mentioned features, such as size, orientation,intensity, predominance with respect to the surround, of an edge,corner, shape, texture, color, object; the position of one of thefeatures in the image or the relative position of two or more of theabove mentioned features; changes in features across consecutiveframes—changes in position (optic flow), intensity, size, orientation;the pitch, intensity, spectral energy in specific hands, formants ofsounds, the temporal changes thereof, disparity measure between two ormore images, input from proximity sensors (e.g., distance, proximityalarm, and/or other), motor feedback (e.g., encoders position), motionsensor input (e.g., gyroscope, compass, accelerometer), previous motorcommands or switching commands, a binary/Boolean categorical variable,an enumerated type, a character/string, and/or practically anycharacteristic of the sensory input.

Mandatory-feature RKNN approach may be utilized for determiningassociations between occurrence of one or more features (also referredto as context) and control output configured to cause an action by arobotic device.

Predicted output associated with individual subsets may be combined(e.g., averaged) to produce predicted output of the RKNN process.Selecting the number of neighbors within a subset, the subset size,and/or the number of subsets may be used to trade-off between speed ofcomputations, and accuracy of the prediction.

By way of an illustration of operation of a robotic device controller(e.g., 400 in FIG. 4A), sensory input (e.g., 402 comprising a sequenceof video frames, inertial motion measurement, motor actuator feedback)may be analyzed using RKNN process in order to determine (predict) motorcontrol signal (418). Sensory input may comprise a plurality of features(e.g., representations of objects determined using video data). In someimplementations, the RKNN process may comprise configuring a pluralityof N KNN classifiers to process randomly selected subsets of features.For a given classifier C_(i) (e.g., 1408 in FIG. 14), a random subsetx_(i) of features may be selected from a pool of potential featuresx={x₁ . . . x_(n)}. As used herein, the term “classifier” may be used todescribe a data processing operation configured to provide an output y(e.g., motor control command) based on analysis of a plurality of inputsx_(i) (e.g., pixels of a digital image).

During training, for a given occurrence of the input x (e.g., sensoryfeatures) and the output y (e.g., training input/correction signal) theassociations may be determined using methodology described with respectto FIGS. 14-15 below.

The selection process may comprise, for a given classifier C_(i) of theplurality of classifiers (i=1 . . . N):

-   -   a) selecting a subset x_(i) of features x, wherein individual        subsets may comprise a mandatory feature (e.g., x_(i) in FIG.        14); and    -   b) appending the entry (x_(i), y) to the classifier C_(i).        In some implementations, individual classifiers Ci may comprise        a table (e.g., the tables 1500. 1530, 1560 in FIG. 15). In the        implementation illustrated in FIG. 14, the first feature x_(i)        may denote the mandatory feature that may be selected for        classifiers Ci in every KNN classifier's feature set. The rest        of the d−1 features may be selected at random from the        population of features (e.g., the input of D features, where        D>d). In one or more implementations, a single classifier C1 may        be configured based on a randomly selected d−1 features from        highly redundant input and one (or more) features from less        redundant input.

During operation, in order to compute the output y for a given input x,one or more (k) entries within individual classifiers Ci may be used todetermine N output values yi of the output y. For a given classifier Ci,individual output yi may be determined based on a first statisticaloperation of the k-values of y obtained during training. In one or moreimplementations, the first statistical operation may comprisedetermination of a mean, median, mode, adaptively weighted mean, and/orother operation. The output y may be determined using a secondstatistical operation configured based on the N outputs yi of individualclassifiers. In one or more implementations the second statisticaloperation may comprise determination of a mean, median, mode, adaptivelyweighted mean, and/or other operation.

FIG. 15 illustrates an exemplary configuration for producing output ypconfigured based on input x using N=3 classifiers and k=4 nearestneighbors. Tables 1500, 1530, 1560 in FIG. 15 may represent threeclassifier Ci instances corresponding to, e.g., index selectiondescribed by elements 1408, 1406, 1404. Individual rows 1502, 1504,1506, 1508, 1510, 1512, 1514, 1516, 1532, 1534, 1536, 1538, 1540, 1542,1544, 1546, 1562, 1564, 1566, 1568, 1570, 1572, 1574, 1576 may denotetraining pairs (x,y) produced during training at time instances t1, t2,t3, t4, t5, t6, t7, tm. In tables 1500, 1530, 1560, X_(ij) may denoteinput wherein:

-   -   index i may denote the classifier (i=1 . . . 3);    -   index j may denote the time instance (j=1 . . . n);    -   yj may denote the training signal, X_(o) may denote input during        operation;    -   yo_(i) may denote individual classifier output; and    -   yp may denote the predicted signal.

For a given time instance, the inputs X_(1,1), X_(2,1),X_(3,1) in rows1502, 1532, 1562, respectively, may be produced using a respectiveplurality of input features (e.g., the input 1402 in FIG. 14). Rows1518, 1.548, 1578, may denote data pairs (x,y) corresponding toclassifier operation configured to produce a predicted output yp basedon occurrence of the input Xo.

Hashed rectangles in FIG. 15 (e.g., as in row 1506) may denote thenearest neighbors as determined during operation of a respectiveclassifier (e.g., 1500). Components 1520, 1550, 1580 may denoteoperation that may be used to determine classifier output. In one ormore implementations, the operations of components 1520, 1550, 1580 maycomprise of one or more statistical operations that may comprisedetermination of a mean, median, mode, adaptively weighted mean,adaptively weighted selection, and/or other methodologies that may beused to determine classifier output (e.g., 1522) based on a plurality(e.g., 4 in FIG. 15) nearest neighbors (e.g., 1506, 1508, 1512, 1514).In some implementations, output yo₁ yo₂ yo₃ of individual classifiers1500, 1530, 1560 may differ from one another due to different nearestneighbor selection. As illustrated in FIG. 15, rows 1506, 1508, 1512,1514 may be selected by the classifier 1500, rows 1536, 1538, 1540, 1542may be selected by the classifier 1530, rows 1566, 1570, 1572, 1574 maybe selected by the classifier 1560. Outputs of individual classifiers1500, 1530, 1560 may be utilized in order to determine the predictedoutput yp using component 1582. In one or more implementations, theoperations of the component 1582 may comprise of one or more statisticaloperations that may comprise determination of a mean, median, mode,adaptively weighted mean, and/or other methodologies that may be used todetermine the output 1582).

The dimension d of the subset xi may be determined based on thedimension D of the input x as follows, in some implementations:

d=floor(√{square root over (D)}).  (Eqn. 15)

Selecting processing parameters (e.g., d, N, k, and/or statisticaloperations) a trade-off between speed and accuracy may be adjusted.

With heterogeneous, multimodal feature vectors, adjusting processingparameters (e.g., d, N, k) may cause modification of the relative impactof the different types of features. By way of an illustration, ifD=1024*1024*3+3, d may be determined using Eqn. 15, (d=1773).Accordingly, individual classifier may be characterized by a probabilityof p=0.0017 of using an audio feature. In order for an audio feature tobe of influence with a level of certainty (e.g., greater than 50%) animpractically large ensemble size N may be required to see any effectsof the audio features.

In some implementations of on-line learning for robot navigation, theinput vector x may be configured by concatenating the RGB values of thepixels in an image (e.g., obtained using video camera 166 in FIG. 1) andan additional 1-channel binary signal derived from the motor state. Themandatory feature (e.g., the feature x1 described above with respect toFIG. 14) may be selected to comprise the 1-dimensional binary motorstate.

In order to facilitate contributions from different types of signals fordetermining a distance measure between features in a metric space (e.g.,Euclidian distance), data from highly redundant input (e.g., the RGBpixel values) may be normalized. Various other distance measures(metrics) may be utilized, e.g., Mahalanobis, Manhattan, Hamming,Chebyshev, Minkowski, and/or other metrics.

In some implementations, the normalization may comprise shifting and/orscaling input features to a given value range (e.g., A1=64 to A2=196 foran 8-bit pixel value, 0 to 1 range, and/or other range). In one or moreimplementations, the normalization may be configured based ondetermining an on-line estimate of the mean and standard deviation offeature values to obtain z-score for individual feature (pixel). In onesuch implementation, for a given pixel (e.g., pixel at location (i1,i2))a pair of values may be stored in history memory: one for the pixel meanand another for the pixel standard deviation. In some implementations,one or more parameters related to history of the input (e.g., pixelstatistics) may be computed over a given interval, and/or the totalduration of training. In one or more implementations, the learningsprocess may be configured to enable a user to reset contents of theparameter (e.g., pixel statistics).

In some implementations, data for one or more inputs may be scaled by aparameter NF, where NF is configured based on the overall number offeatures of a given feature types (i.e., the number of pixels in asubset t). In some implementations, the scaling parameter may beselected from the range between √{square root over (NF)} and 10×NF.

In some implementations, feature scaling operation may comprisedetermining an average distance measure for a plurality of input featureinstances (e.g., distance between 2-100 images for images acquired at 25fps) and scaling the input in accordance with the average distancemeasure. Various scaling implementations may be employed, e.g., scalingthe less redundant input, scaling the highly redundant input, and orcombination thereof. The scaling operation may enable reducing disparitybetween contributions to the distance determination from a highlyredundant input (e.g., video and/or other input) and less redundantinput (e.g., audio, touch sensor, binary, and/or other input).

The feature scaling may be configured based on an observed and/or anexpected characteristic or characteristics of a feature that may besalient to the action. By way of an illustration of an implementation ofvision based robotic navigation, size of a target, e.g., number ofpixels and/or cumulative pixel value corresponding to a ball 174 in FIG.1, may be used to scale pixel values within a visual frame such thatpixels of the target associated with the resealed input may contributecomparably to the distance determination as a binary input feature(e.g., indicative of wheel rotation (or not), proximity sensor output(ON, OFF), battery level below threshold, and/or other binary input). Insome implementations, the scaling configured based on observed and/orexpected characteristic of a feature may be referred to as inputequalization.

When determining feature-action associations, traditional RKNNmethodologies of the prior art may discount data provided via sensormodalities (e.g., audio, touch) characterized by fewer dimensions (fewerfeatures) compared to other modalities (e.g., video). In someimplementations of the present disclosure, a normalization operation maybe applied to data of individual sensory modalities. The normalizationoperation may be used to increase and/or decrease contribution of dataof one modality relative contribution of data of another modality to theRKNN distance determination. In some implementations, the normalizationmay comprise selecting a given number of mandatory features (e.g., thefeature xi described above with respect to FIG. 14). Selecting a numberin of mandatory features may ensure that at least in out of d featuresmay contribute to distance determination. In the exemplaryimplementation described above with respect to FIG. 14, probability ofthe mandatory occurrence in the feature subset x_(i) is equal one: P=1.Probability of occurrence of the remaining features in the subset x_(i)is P0<1.

In some applications wherein data from two modalities with greatlydifferent number of features (e.g., video and audio) may be used withRKNN, distance between any two samples may be dominated by the sensorymodality with greater number of features (e.g., video).

Equalization may be applied so that contribution of individual sensorymodality on expected distances may be comparable relative contributionfrom another modality data. In some implementations, the equalizationmay comprise determining an on-line estimate of the mean and standarddeviation of individual features; using the on-line estimates tocalculate a normalizing constant Cs for individual sensory modality ssuch that the expected Euclidean distance between two samples, measuredonly using the features in modality s is 1.0. Weighting data of a givenmodality (to further reduce the mean squared error) as trainingparameters that may be optimized during training.

RKNN approach may be employed for determining the relative importance ofthe features for producing a given output. Feature relevance may bedetermined based on an error measure produced by individual KNNclassifiers that contain those features. In some implementations, morerelevant (e.g., “better”) feature for a given output, may correspond toa lower error of individual KNN classifier(s) that may contain thatfeature.

In some implementations, e.g., such as described with respect to FIGS.14-15, computational load on a feature detection system may be reducedby selecting a small number of classifiers N, e.g., N<D/d, so that aportion of the total available features may be used by a givenclassifier instance. In some implementations, the number of classifiersmay be selected from range between 3 and 10, and number of used featuresmay be selected between 5% and 50% of the total available features. Byway of an illustration, for input comprising a digital frame of 12×12pixels and three color channels (e.g., RGB, YUV, and/or other colormodel), using N=5 classifiers corresponds to d=floor(√{square root over(12×12×3)}=20, features per classifier. Accordingly, d*N/D=5*20/432=23%of the available features from the original data may be used.

In one or more implementations, the computational load for aclassification system may be characterized by being able to performbetween 10 and 20 classifications per second (CPS) processing videoinput comprising a sequence of RGB frames of 12×12 pixel resolutionrefreshed at 25 frames per second. The processing system may comprise anembedded computer system comprising a processing component (e.g.,Qualcomm Snapdragon 805/806) comprising a CPU component capable ofdelivering 210 Mega-Floating-point Operations Per Second (MFLOPS) and aGPU component capable of delivering 57 GFLOPS with maximum combinedpower draw of no more than about 2.5 W.

In some implementations, the RKNN may be utilized in order to determinea feature ranking parameter at a target rate (e.g., 15 CPS) whileconforming to the processing load capacity and/or power draw limit byperiodically re-initializing individual KNN classifiers on a rotatingbasis (i.e., not all at once) with a random set of features.

In order to re-populate the KNN classifier subsets (e.g., 1404, 1406,1408 in FIG. 14), a history buffer may be utilized in order to storepreviously occurring training data (e.g., instances of the input 1402 inFIG. 14). Upon producing the updated random indexes, the featurerelevance may be Obtained using the history buffer data. In someimplementations, an updated set of features may be determined randomly“just in time,” or everything could be scheduled at once when the wholeensemble is first initialized to deterministically establish how muchdata would be used to calculate feature importance.

In some implementations of RKNN classifiers, feature assignment for aKNN classifier may be biased using a random process. By way of anillustration, random process used for selection of indexes for aclassifier may be biased to increase probability of features with ahigher utility within the input (e.g. 1402 in FIG. 14) to be included inthe subset (e.g., the subsets 1404, 1406, 1408). The magnitude of thebias regulating the trade-off between how quickly the subset mayconverge to a set of features versus how much time the subset may spendexploring new combinations of features.

In one or more implementations of RKNN, ensembles evolutionaryalgorithms (EA) may be employed. The evolving population may comprisepopulation subsets of the classifiers. The genotype/phenotypecharacterizing the EA process may comprise the particular subset offeatures chosen for a given classifier. Low-utility classifiers may beculled from the population. New classifiers are may be produced byrecombining and/or mutating the existing genotypes in the population ofclassifiers. The EA approach may produce a higher-performing ensemble ofKNN classifiers, compared to existing approaches.

Apparatus and methods for behavioral undo during training of robots aredisclosed herein, in accordance with one or more implementations. Insome implementations, a robotic device may comprise a controlleroperating a software component (e.g., the BrainOS® software platform)configured to enable training. A user may control/training the robotwith a remote device (e.g., comprising a Gamepad® controller and an iOS®application, and/or a handset device (e.g., a smartphone)). Training ofthe robot's controller may be based on the user observing robot'sactions and sending one or more target control commands to the robot viathe training handset. The trained controller of the robot may comprise atrained configuration configured to enable autonomous operation (e.g.,without user input) by the robotic device. The trained configuration maybe stored. A saved configuration may be loaded into the robot beingtrained thereby providing one or more trained behaviors to the robot. Ensome implementations, the trained configuration may be loaded to one ormore other robots in order to provide learned behaviors. Subsequent toloading of the saved configuration, the controller learning process maymatch process configuration being present during saving of theconfiguration.

In some implementations, the BrainOS configuration may be stored (saved)automatically based on timer expiration (e.g., periodic saving) and/orbased on an event (e.g., triggered by a user and/or based on a number ofissued control commands.

The autosave timer interval T may be configured by the user via, e.g.,interface of the training handset. In some implementations, the user mayconfigure the controller process to save BrainOS configuration when theuser may issue a command (correction) to the robot using the traininghandset. In one or more implementations, the training configuration maybe saved upon receipt of n commands from the user (n≧1).

In some implementations, user commands (corrections) may arrive in oneor more clusters (e.g., a plurality of commands) that may be interleavedby periods of user inactivity (e.g., training a race car to traverse aracetrack). In one or more implementations, a given command (e.g., thefirst, the last, and/or other command) in the cluster may trigger savingof the configuration.

In one or more implementations, the BrainOS may be configured to executeperiodic and event-based autosave mechanisms contemporaneously with oneanother.

Trained behaviors of the robotic device may be configured based onlearning of associations between sensory context (e.g., presence of anobstacle in front of the robotic vehicle) and a respective action (e.g.,right turn) during training.

It may be beneficial to remove one or more trained behaviors from thetrained configuration of the controller. In some implementations, thetrained behavior removal may be based on one or more of performancebelow a target level, changes of the robot configuration (e.g.,replacement of a wheel with a skate), changes in the robot'senvironment, learning of erroneous associations, and/or other causes.

The BrainOS software platform may be configured to save one or moreparameters characterizing the learning process and/or the learnedbehaviors. In some implementations, the saved parameters may be used toproduce (recreate) the BrainOS instance, for example, specify thesensory processing algorithms used for learning, describe learningalgorithms. In one or more implementations, the saved parameters may beused to characterize learning parameters (e.g., the learning rate,weights in an artificial neuron network, entries in a look up table,and/or other parameters).

For example, the configuration saving may comprise storing of weights ofa neural network may characterize mapping of the sensory input to motoroutputs; and/or weights of feature extractor network component that maybe used to process the sensory input.

The BrainOS software platform may be configured to enable users toselectively remove a learned behavior (and/or a portion thereof) via anundo and/or time machine operation.

At a given time, a user indication may be used to trigger an UNDOoperation. In some implementations, the UNDO operation may compriseloading of the previously saved configuration. By loading at time t1 theconfiguration saved at time t0<t1, the robot controller effectively‘forgets’ what it learned in time interval t0<t<t1.

The user UNDO indication may be configured based on one or more of theuser activating a user interface element (e.g., a physical and/orvirtual touch-screen button), a voice command, a gesture, and/or otheractions, in one or more implementations. One or more undo indicationsmay be utilized in order to remove multiple behaviors (and/or multipleversions of a given behavior). By a way of an illustration, pressingCtl+Z in a MS Word® may effectuate UNDO of successive edits. Similarly,providing a plurality of UNDO indicating may cause removal of multiplelearned associations.

In one or more implementations, the undo operation may be effectuatedusing a timeline comprising, e.g., a plurality of bookmarks (e.g., shownin FIG. 5) indicative of one or more date/time, context, action, and/orother attributes of association learning. A user may select a givenbookmark in order to restore (undo) the learning configuration to thestate corresponding to time of the bookmark. For example, user may tapon a selected marker (representing a saved state) a slider may be usedto navigate on this timeline combination of above.

Combiner apparatus and methods are disclosed herein, in accordance withone or more implementations. En some implementations of supervisedtraining of robots, control instructions (also referred to ascorrections) produced by the trainer (e.g., human) may be combined withcontrol instructions produced by the robot controller instructions(predictions).

In some implementations, the trainer may be provided with the control ofthe robot during training. Upon completion of the training, the robotmay be configured to operate autonomously. In one or moreimplementations, training may comprise periods of autonomous operationand periods of learning, wherein trainer's control input may be combinedwith the robot's internally generated control.

The BrainOS software platform may be configured to enable onlinelearning wherein trainer's input may be combined with the internallyproduced control instructions in real time during operation of therobotic device. That is, the input from the trainer may be applied tohave an “on-line” effect on the robot's state during training. The robotnot only learns to move forward in this sensory context, but it alsoactually moves forward into some new sensory context, ready to be taughtfrom the new location or configuration.

By way of an illustration, when training a remotely controlled car usinga joystick, the car may be trained to navigate a straight trajectory(e.g., autonomously move forward). Subsequently, a trainer may elect tocommence training of one or more turn behaviors (e.g., turnleft/right/turnaround/drive in a circle and/or other maneuvers). Thetrainer may use the joystick to provide left/right turn commands to thecar to train it to turn. In one or more implementations, the trainer mayassume the control during the turn action and/or provide the turninstructions incrementally (e.g., in three 30° increments to complete90° turn).

Conversely, the car may be trained to follow a circle. In order to trainthe car to follow a straight line the trainer may utilize the joystickto provide the training input. In some implementations, the trainer mayutilize the joystick forward position in order to override the carinternal control input and to cause it to proceed forward. In one ormore implementations, the trainer may utilize the joystick left/rightposition in order to provide an additive control input so as to guidethe car to proceed in a straight line.

Controller of the robot may comprise a combiner component configured toeffectuate the process of combining the training input (correction) withthe internally generated control (prediction). In some implementations,the combiner may be configured to allocate a greater priority (e.g.,larger weight) to the correction input, e.g., to implement “the traineris always right” mode of operation. When the robotic platform (e.g., thecar) may comprise multiple degrees of freedom (DOE), the trainingprocess may be configured to operate (e.g., train) a given DOF at agiven time.

In some implementations, the combiner component may be operable inaccordance with a Full Override process, wherein input by the trainertakes precedence (e.g., overrides) the internally generated (predicted)control signal. When operable in the override mode, the controller maylearn the context-action association and produce predicted controlsignal. However, the prediction may not be acted upon. By way of anillustration of training a robot to traverse an obstacle course, thefull override combiner may enable the trainer to communicate to thecontroller of the robot which actions to execute in a given portion ofthe course given the corresponding sensory context (e.g., position ofobstacles). Use of the Full Override combiner process may reduce numberof trials required to attain target level of performance, reduceprobability of collisions with obstacles thereby preventing damage tothe robot.

In some implementations, the combiner component may be operable inaccordance with an Additive Combiner process. When operable in theAdditive Combiner mode, the trainer's control input may be combined withthe predictor output. In some implementations, the trainer's input andthe predicted control may be configured in “delta” space wherein thecontrollable parameter (e.g., correction 408 in FIG. 4A) may be used tomodify the existing state of the system (e.g., comprising motor torqueand/or robot platform acceleration) rather than indicating a targetvalue (setpoint). In some implementations, the delta control approachmay be utilized with a continuously varying robot state parameter (e.g.,speed, orientation). In one or more implementations, the delta controlapproach may be used for manipulating a discrete state (e.g., training acontroller of an elevator).

For example, if the target angle is 45°, the trainer's input mayinitially exceed the target angle in order to reduce learning time.Subsequently as the robot begins to move its current trajectory towardsthe target (e.g., towards 45°), the trainer may reduce the input anglein order to prevent overshooting the target trajectory angle.

The Additive Combiner process may advantageously enable training of aone DOF at a given time instance thereby facilitating training ofrobotic devices characterized by multiple DOF. During training of therobot using the Additive Combiner process, the trainer and the robotcontribute to the output (execute action). The trainer may adjudge thelearning progress based on a comparison of the trainer's contributionand the action by the robot. The Additive Combiner process mayfacilitate provision of small corrections (e.g., heading change of a fewdegrees to direct the robot trajectory along 45° heading). In someimplementations wherein default state of the robot's controller may becapable of providing control output that may operate the robot within arange from the target trajectory (e.g., drive forward in a straightline). The Additive Combiner may provide an economical approach tocorrecting the default trajectory to the target trajectory. By way of anillustration, natural predisposition of a randomly-initialized neuralnetwork may be sufficient for some behaviors (e.g., the neural networkmay have a tendency to turn away from certain obstacles withouttraining.) This means that memory resources (e.g., weights) of thelearning controller process may not have to be modified in some cases.When the predictor may select an action that may be acceptable to thetrainer, network memory modifications may not be required. The networkmay be idiosyncratic in the way it performs certain tasks or actions,but reduced computational resources are required for achievingperformance.

During training of a robot by a human trainer using the AdditiveCombiner, the teacher may encounter an appealing experience as the robotmay begin to take over (assist) as the training progresses. Suchexperience may encourage the trainer (particularly a novice) to performtraining of robots.

In some implementations, the combiner (e.g., 418 of the controller 400in FIG. 4A) may be operable in a Touchfader Override (TO) mode. Whenoperable the TO mode the combiner may weigh the trainer's input (408)with a greater weight compared to the predicted signal (418) therebyimplementing the override functionality. The TO combiner implementationmay comprise a user interface configured to (i) convey to the trainerconfiguration of the combiner (e.g., weights associated with theteaching input and predictor output); and enable the trainer to providesmall (e.g., less than 20% of the combined value) correctiveadjustments. The interface illustrated in FIGS. 13A-13B may be utilizedwith, e.g., delta-based control (e.g., varying motor torque) and/orposition-based control (e.g., varying robot's position).

In some implementations, e.g., such as illustrated in FIG. 13C, theTouchfader combiner may comprise a physical interface wherein thecurrent value of the control changes may be provided in a manner visibleto the user by moving the physical element (a physical slider, a knob(e.g., 1361 in FIG. 13C, and/or another control element). The trainermay apply a small amount of force to the physical control element inorder to make slight adjustments (corrections), or a large force to makesubstantial adjustments (overrides).

In some implementations, e.g., such as illustrated in FIG. 13B, theTouchfader may comprise a proximity sensitive interface (e.g., atouchscreen) effectuated using a static (e.g., built in appliance)and/or portable computing device (e.g., a smartphone). In someimplementations, the touchscreen interface may provide magnification ofcontrollable element (e.g., area around the location of the currentcontrol) this simulates the resistance of the physical control, but onan easier-to-implement touch screen.

In some implementations, the touchfader combiner may comprise overridingcontrol methods, the user can implement “virtual additive” function bytouching the screen just a bit to the left or to the right of theslider's current position.

In one or more implementations, the combiner (e.g., 414 in FIG. 4A) maycomprise an Additive-Override Hybrid (AOH) process. The AOH process mayfunction as an additive process for small (e.g., 10% or less of thetotal value) corrections. For inputs that are in excess of thethreshold, the AOH process may implement an overriding combinerfunctionality. In some implementations wherein the predictor p, and theteaching c (corrector) signals may be configured in the range between −1and 1, the AOH process logic may be expressed as follows:

If p>R×c, b=c; else b=p+c;,  (Eqn. 16)

where b denotes the combiner output (e.g., 430 in FIG. 4A), and Rdenotes the threshold (selected from the range between 0 and 0.2 in someimplementations). In one or more implementations, the AOH combiner maycomprise an interpolation operation configured to provide a smoothtransition between branches of the logical expression of Eqn. 16.

In some implementations, the interpolation may be expressed as follows:

$\begin{matrix}{b = {p + c - {p\left( \frac{\left| {p - c} \right|}{\left| p \middle| {+ |c|} \right.} \right)}}} & \left( {{Eqn}.\mspace{14mu} 17} \right)\end{matrix}$

where

p, predictor signal in [−1 1];

b, motor control (combiner output) signal in [−1 1];

c, corrector signal in [−1 1].

In some implementations, the combiner may be operable in accordance withthe Threshold Nonlinearity (TN) process. The TN combiner process may beconfigured to provide additive and override functionality depending onthe relative magnitude of the correction and prediction components, insome implementations, the TN combiner operation may be configured asfollows:

b=p+c, b=1 when b>1; b=−1 when b<−1;  (Eqn. 18)

where

p, predictor signal in [−1 1];

b, motor control (combiner output) signal in [−1 1];

c, corrector signal in [−2 2] range.

The combiner of Eqn. 18 may be operated to provide additivefunctionality. A threshold nonlinearity of the combiner of Eqn. 18 maybe configured such that large corrector input (in excess of the maximummagnitude of the predicted component, e.g., 2) may be used to overridethe predictor component. By way of an illustration of an autonomousrobot approaching an obstacle, when predicted output (e.g., −1) maycause a collision with the obstacle, an additive combiner with maximumcorrection signal value of 1 may be unable to prevent the collision.Using corrector signal range (e.g., from −2 to 2) that may exceed thepredictor signal range (e.g., from −1 to 1) and the combined signalrange (e.g., from −1 to 1). In the above example, the correction inputof 2 may be used to effectively override the (erroneous) predictedoutput and guide the robot away from the obstacle.

The combiner of Eqn. 18 may be employed with the delta control processwherein the controllable parameter (e.g., correction 408) may be used tomodify the current value of the system state (e.g., vehicleacceleration, motor torque, and/or other parameter) rather thanindicating a target value (setpoint). In some implementations, the deltacontrol approach may be utilized with a continuously varying robot stateparameter (e.g., speed, orientation). In one or more implementations,the delta control approach may be used for manipulating a discrete statespace controller (e.g., controlling an elevator, a pick and placemanufacturing robot, a shelve stocking robot and/or other controlapplication).

Systems and methods for training path navigation are disclosed herein,in accordance with one or more implementations. Exemplaryimplementations may provide a mechanism to stabilize the trajectory of arobot to keep it on the desired path. In some implementations, the robotmay use a camera. The robot may store a sequence of images seen duringthe training phase in an ordered buffer. For individual images, therobot may recall motor commands that were issued at a point in timecorresponding to a given image.

In response to a new camera image arriving during autonomous behavior,the robot may compare it with the images in the training buffer. Therobot may find the most likely match. The robot may evaluate, whetherthe image is shifted left or right compared with the stored image aspreviously seen. If the new image is shifted left, the robot may need tomake a right turn, and vice versa. An example of this is shown inIllustration 1, in accordance with one or more implementations.

Exemplary implementations may be equally applicable regardless ofwhether the shift is due to the robot being located physically to theleft, or in the same physical location (or even in a position to theright) but turned towards the left. To illustrate, assuming the camerafaces straight ahead, the center of the image may be the spot which therobot is headed towards. Thus, if this spot is to the left of the spotwhere the robot is supposed to be headed towards (as defined by thecamera image seen during training), then the robot may need to adjustits heading rightwards.

To determine whether the new image is shifted left or right comparedwith a stored image, the robot may determine the cross-correlationbetween the two images. The argmax of the cross correlation maycorrespond to the most likely x,y shift amount. If the images areidentical except for a shift, then the cross-correlation may be 1 at thecorresponding x,y shift.

The cross-correlation may be determined by utilizing the spatialfrequency domain. According to some implementations, a windowingfunction may be applied to individual images to reduce edge effects. Afast-Fourier transform (FFT) may be performed to obtain a spatialfrequency representation. The normalized cross-power spectrum may bedetermined. An inverse FFT may be applied to return to x,y domain.

Determining the image shift amount may be used in the image comparisonmetric that determines which images from the stored training buffer areconsidered good matches with the new camera image. For example, one suchmetric may be to shift the new camera image by a determined shift amountand determine the norm of the image difference in the overlappingregion.

Ideally, the shift amount should stay at 0 as the robot moves, whichwould mean that the robot stays on track. The shift amount may beconsidered as an error metric. The control variable may be the steeringsignal, which may be adjusted leftwards or rightwards, in someimplementations. A negative feedback loop may be used to cause the errorto converge to zero and keep it stable around that level. In someimplementations, a PID controller may be a practical example of acontroller that accomplishes this.

In some implementations, the motor commands at a given time step may beobtained by taking the stored motor commands from the training bufferthat are associated with the best matching stored image. Those motorcommands may be added to the output from the PID controller to stabilizeit.

As the robot is following a learnt path, it may expect to seeapproximately the same camera images in the same order as seen duringtraining. The robot may not instantaneously jump from one part of thepath to another. It may be useful to determine and take into accountprior information about which sequence number(s) of the training bufferare the most likely. The assigned likelihood of a new camera imageactually being taken from the same location as a particular image in thebuffer of training images, may be related to how well the new imagematches up with the stored image as well as how likely that location wasin the first place according to the prior information, as shown anddescribed with respect to FIG. 21.

For computational efficiency reasons, it may not be desirable orfeasible to compare each new camera image with every one of the storedimages seen during training, according to some implementations. Therobot may take advantage of the prior information about what are thelikely regions of the path where it might be located, and only searchthose regions. The robot may search a random sample of other regions incase the prior information is inaccurate or invalidated for some reason.

In some implementations, the search space may be narrowed using a formof particle filtering, where the robot maintains a multitude ofparticles indicating the likely parts of the path. That is, individualparticle points at a particular image from the training buffer. As a newcamera image arrives, the robot may search those images in the trainingbuffer which are close to the particles. Individual particles may bemoved to a nearby location in the training buffer where the stored imagematches closely with the newly arrived image. Particles with poor matchwith the new image may be deleted. New particles may be created, eitherin the vicinity of the other particles, or from randomly sampledlocations in the training buffer, shown in FIG. 22.

In some implementations, a robot may be trained to follow a path. Theimage shift determination may inform the robot of whether the robot istoo far off to the left or right. The robot may adjust its heading tocompensate. A PID controller may be used to add necessary negativefeedback to make the system stable in following the path, in someimplementations. Prior information about where in the training sequencethe robot is currently operating may guide the robot in making correctinferences about new camera images, and may help the robot narrow thesearch space to gain computational efficiency.

In FIG. 20, according to some implementations, the image ‘new’ may bethe camera image seen during autonomous behavior. The image ‘old’ may bethe closest match found in the buffer of training images. The image‘shift’ may show how the ‘new’ image is shifted to obtain a good matchwith the ‘old’ image. The amount of shift in x and y direction may bedetermined by a phase correlation calculation. With the images on theleft side, the camera image may be shifted left, which may imply therobot needs to adjust its heading right. With the images on the rightside, it is the other way around.

FIG. 21 presents an exemplary sequence of number changes over time asthe robot moves, in accordance with one or more implementations.According to some implementations, the x-axis may denote a time step.The y-axis may show the sequence number of the image from the trainingbuffer chosen to be the most likely match. In this case, the trainingphase may have included repeating the desired path 3 times. Thus, at anygiven time, there may be three locations in the training buffer whichmatch well with the new camera image. That may be why the selectedsequence number is seen to jump between three more or less parallel,virtual lines.

FIG. 22 According to some implementations, the x-axis may be the timestep, and the y-axis may be the sequence number of the image in thetraining buffer that the particle is pointing to. Only particles with alifetime of at least 10 steps were included. Here the parallel, virtuallines may be seen more clearly than in FIG. 21, because the particlesmay track all of them simultaneously.

Using this method, the estimate that the vehicle is in a given locationcan include evidence from previous frames, as accrued by each particle.For example, assuming independent noise across frames, a more robustestimate of the error in position could be achieved by calculating theproduct of the likelihood that the sensor data came from a givenparticle over the recent frames. Likelihood may be approximated using anexponentiated energy model, or explicitly calculated with a parametricstatistical model. Particle deletion could be implemented using atemporally decaying cumulative log probability that deletes a givenparticle when the probability is lower than a fixed threshold.Additional techniques in rejection sampling (e.g. similar toMetropolis-Hastings process) sampling may be used to define a threshold.

Systems and methods for providing VOR for robots are disclosed herein,in accordance with one or more implementations. Exemplaryimplementations may provide VOR-like functionality for a robot. In someimplementations, VOR for a robot may refer to the stabilization of thecamera image while the robotic body is moving. In existing roboticplatforms Where the movement of the system might be subject tounexpected disturbances (e.g. quad copter, two-wheeled robot (e.g., aSegway-type configuration), and/or other robotic platforms), thisstabilization may improve the quality of the camera signal. Exemplaryimplementations may, for example, reduce blurring associated with themotion of a camera. The cleaned camera image may be later used forvarious applications (e.g., recording of stable video footages, cleansensors data for better post processing, and/or other applications).

Image stabilization (IS) may include a family of techniques used tocompensate for pan, tilt, and roll (e.g., angular movement, equivalentto yaw, pitch and roll) of the imaging device. That family of techniquesmay include one or more of optical image stabilization, digital imagestabilization, stabilization filters, orthogonal transfer CCD, camerastabilizer, and/or other techniques.

In some implementations, a camera stabilizer may utilize a set gimbaldevice. According to some implementations, a gimbal may be a pivotedsupport that allows the rotation of an object about a single axis. A setof three gimbals mounted with orthogonal pivot axes may be used to allowan object mounted on the innermost gimbal to remain independent of therotation of its support.

The system may use a physical camera stabilizer to solve the problem ofstabilizing the camera mount, in some implementations. This approach mayenable VOR-like functionality on a robot with low cost sensors (e.g.,gyroscope, accelerometer, compass, and/or other sensors) and low costactuators (e.g., open loop control system, no feedback from the servos,and/or other actuators). In comparison, existing systems typicallyeither use a fairly complex and expensive mechanical system (e.g., agimbal camera) and/or a computationally expensive software solution thatare not adapted to small robots with embedded low-powered processingboards.

Exemplary implementations may be not computationally expensive and mayprovide one or more of the following properties: change the center ofthe visual field dynamically, compensate selectively for unexpectedmovements versus desired movements, dynamic activation and deactivationof the VOR-like functionality, compensate for sensory motor delays ifcouple with a predictive model, and/or other properties.

Some implementations may assume that the camera to be stabilized ismounted on a set of one, two, or three servos, wherein an individualservo is allowed to rotate the camera on one axis (e.g., pan, tilt, orroll). The combination of servos may provide up to three degree offreedom for the stabilization of the movement of the camera.

The figure below illustrates an exemplary architecture used toaccomplish the VOR-like functionality stabilization of a camera image,in accordance with one or more implementations.

The VOR-like functionality module may integrate inputs from sensors(e.g., state of the system, blue box) and higher level signal (e.g.,sensorimotor control systems, red box) to determine the correction anddesired position of the camera to stabilize the image (e.g., cameraservos position, right part of the diagram).

The state of the robot may be provided one or more sensors that providethe global orientation of the robot and/or a derivative of the globalorientation in multiple axes. Some implementations may include one ormore of a gyroscope, an accelerometer, a magnetometer, and/or othersensors. A gyroscope may include a device that measures orientationchanges, based on the principles of angular momentum. Someimplementations may utilize a three-axis gyroscope, which may providethe velocity of change in the three directions x, y, and z. Anaccelerometer may include an electromechanical device that measuresacceleration forces. These forces may be static, like the constant forceof gravity pulling at your feet, or they could be dynamic, caused bymoving or vibrating the accelerometer. By measuring the amount of staticacceleration due to gravity, the angle the device is tilted at withrespect to the earth may be determined. A magnetometer may include adevice that measures the direction of the magnetic field at a point inspace. In some implementation, the system may include a three-axismagnetometer.

The higher level inputs may be provided by a sensorimotor controlprocess, which may control the desired movement of the robot (e.g.,output of the motor control system) and/or the desired focus point ofthe camera output of the vision control system).

The motor control system may represent any process and/or devicesconfigured to send a motor command to the robot. A motor command may,for example, be represented in a different space (e.g., a desired setpoint, a new desired linear and angular velocity for a wheeled robot, atorque command, and/or other representations). A motor control systemmay, for example, include one or more of a wireless joystick connectedto the robot, a process that configured to follow a pre-defined path, alearning system, and/or other control mechanisms.

The vision control system may represent any process and/or deviceconfigured to update the focus point of the camera to be stabilized,and/or to switch on and off the VOR-like functionality module. In someimplementations, a vision control system may include a handheldcomputing device (e.g., a tablet computer, a Smartphone, and/or otherhandheld device) where the user can tap on the screen displaying thecamera stream the position where the camera image should be center,and/or an automatic tracker that follows an object of interest in thevisual field.

At individual time steps, the VOR-like functionality module may receivethe change of orientation since the last step, as well as the new motorcommands. In this stage, the focus point may be assumed to be fixed andbe set for each servo. FIG. 24 presents a logical flow diagramdescribing operations of the VOR process, in accordance with one or moreimplementations. Depending on the frequency and amplitude of movement,the VOR module may need to run at a high frequency (e.g., at 100 Hzand/or other frequencies).

In some implementations, the process may run in an infinite loop, andmay exit the loop responsive to the main program of the robot beingstopped. Before entering the loop, the desired position for individualservos may be set to the actual position of the servo. This may suggestthat, in the absence of movement, the servo should not be moved.

If the VOR module is activated, new sensors values may be provided, anew orientation of the robot may be updated, and the change oforientation on dt may be determined, according to some implementations.The motor command may be sent to the robot and signals to the nextmodule may be provided in order to update a new desired position.

The next stage of the VOR process may be to update the new desiredposition of individual servos. The desired position may account for (i)un-expected movement (such displacement should be compensated) versus(ii) desired movement where the VOR-like functionality should becounter-compensated. For a given servo i, this may be achieved by atwofold process, in some implementations. First, the desired position ofthe given servo may be added to or otherwise combined with the velocityof change for the particular axis multiplied by dt and a gain that isservo dependent (k1 [i]). Second, the amplitude of the desired movementmay be removed along individual axes multiplied by dt and a gain that isalso servo dependent (k2[i]). Some implementations may assume knowledgeof how a given motor command will affect the camera movement in eachdirection.

The new desired position may be provided to individual servos of thecamera mount. The desired position may be decayed so that it slowly getsback to the focus point overtime. This may facilitate compensating overtime drift due to error measurement stemming from noise in the sensors.The gain k1 and k2 may not have to be perfect, in some implementations.

In some implementations, k1 and/or k2 may not be a constant to achieveperfect compensation, but instead may exhibit a slow drift toward thefocus point.

In some implementations, the focus point of the camera may changedynamically by another process using the VOR module. Someimplementations may include coupling the VOR system with a tracker(e.g., OpenTLD, MIL, and/or other tracker) such that the image isstabilized on the object of interest. Some implementations may involvecoupling the VOR system with a user interface to control cameraposition. Such an interface may be a physical interface (e.g., ahead-mounted device such as an Oculus Rift) configured to allow the usermoves his/her head to define the new position and get the feedback fromthe camera on the head screen. Some implementations may include couplingthe VOR system with a vision control system, making sure that the robotwill look to a direction perpendicular to the acceleration vector (inthe horizon).

The focus position of the camera may be a variable that can be updatedby the vision control system. In this case, in the absence of unexpectedmovement, the “decay desired position” module may cause the camera todrift to the new position.

Compensation for sensory-motor delays may be included in implementationsof the system. Some implementations may include a predictive moduleconfigured to prevent sensorimotor delays and/or components ofun-desired movement that can be predicted based on the input of othersensors (once it is integrated). For example, according to someimplementations, if the system goes into an oscillatory behavior, mostof the oscillation may be predicted and compensated once it kicks on.

In some implementations, information from the gyroscope may be utilizedto compensate for movement. In some implementations, a sensor fusionprocess may be utilized to integrate that information and improve thecompensation.

The sensor fusion module may obtain a measurement from one or more of anaccelerometer, magnetometer, gyroscope, and/or other source. The sensorfusion module may integrate the measurement(s) using a sensor fusionprocess to give an accurate estimation of the orientation of the systemin space. The following figure illustrates an exemplary sensor fusionprocess, in accordance with one or more implementations.

FIG. 25 presents exemplary code in the Python language that may beutilized with a two-wheeled, self-balancing, robotic platform (e.g.,similar to a Segway-type configuration), compensating for pan and tilt,in accordance with one or more implementations.

Implementations of the principles of the disclosure may be applicable toa wide assortment of applications including computer-human interaction(e.g., recognition of gestures, voice, posture, face, and/or otherinteractions), controlling processes (e.g., processes associated with anindustrial robot, autonomous and other vehicles, and/or otherprocesses), augmented reality applications, access control (e.g.,opening a door based on a gesture, opening an access way used ondetection of an authorized person), detecting events (e.g., for visualsurveillance or people or animal counting, tracking).

A video processing system of the disclosure may be implemented in avariety of ways such as, for example, a software library, an IP coreconfigured for implementation in a programmable logic device (e.g.,FPGA), an ASIC, a remote server, comprising a computer readableapparatus storing computer executable instructions configured to performfeature detection. Myriad other applications exist that will berecognized by those of ordinary skill given the present disclosure.

Although the system(s) and/or method(s) of this disclosure have beendescribed in detail for the purpose of illustration based on what iscurrently considered to be the most practical and preferredimplementations, it is to be understood that such detail is solely forthat purpose and that the disclosure is not limited to the disclosedimplementations, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present disclosure contemplates that, to the extent possible, one ormore features of any implementation can be combined with one or morefeatures of any other implementation.

What is claimed is:
 1. A method of determining a control signal for arobot, the method being performed by a special purpose computingplatform having one or more processors executing instructions stored bya non-transitory computer-readable storage medium, the methodcomprising: receiving first input features of first type and secondinput features of a second type; determining a subset of features byrandomly selecting a portion of the first input features and at leastone feature from the second input features; comparing individualfeatures of the subset to corresponding features of a plurality oftraining feature sets, individual ones of the plurality of trainingfeature sets comprising a number of training features, the number beingequal to or greater than the quantity of features within the subset offeatures; based on the comparison, determining a similarity measure fora given training set of the plurality of training feature sets, thesimilarity measure characterizing similarity between features of thesubset and features of the given training set; responsive to thesimilarity measure breaching a threshold, selecting one or more trainingsets from the plurality of training sets; determining one or morepotential control signals for the robot, individual ones of the one ormore potential control signals being associated with a correspondingtraining set of the plurality of training sets; and determining thecontrol signal based on a transformation obtained from the one or morepotential control signals; wherein: individual ones of the plurality oftraining feature sets comprise features of the first type and at leastone feature of the second type; individual ones of the plurality oftraining feature sets are obtained during training operation of therobot, the training operation being performed responsive to receiving atraining signal from the robot; and individual ones of the one or morepotential control signals being determined based on the training signaland the features of the given training set.
 2. The method of claim 1,wherein the similarity measure is determined based on a differencebetween values of the features of the subset and values of the featuresof the given training set.
 3. The method of claim 1, wherein thesimilarity measure is determined based on a distance metric betweenindividual features of the subset of features and corresponding featuresof the given training set.
 4. The method of claim 3, wherein selectingone or more training sets comprises selecting a training set associatedwith a smallest distance metric.
 5. The method of claim 3, whereinselecting one or more training sets comprises selecting N training setsassociated with a lowest percentile of the distance metric, N beinggreater than two.
 6. The method of claim 5, wherein the transformationcomprises a statistical operation performed on individual ones of theone or more potential control signals associated with the selected Ntraining sets.
 7. The method of claim 6, wherein the statisticaloperation is selected from the group including mean and percentile. 8.The method of claim 5, wherein the transformation comprises a weightedsum of a product of individual ones of the one or more potential controlsignals and a corresponding distance measure associated with theselected N training sets.
 9. The method of claim 1, wherein: the controlsignal is configured to cause the robot to execute the action; the firstinput type comprises a digital image comprising a plurality of pixelvalues; and the second input type comprises a binary indicationassociated with the action being executed.
 10. The method of claim 9,wherein: the training comprises a plurality of iterations configuredbased on the training signal; and a given iteration is characterized bya control command and a performance measure associated with the actionexecution based on the control command.
 11. The method of claim 9,wherein: the plurality of pixels comprises at least 10 pixels; and therandom selection is performed based on a random number generationoperation.
 12. A self-contained robotic apparatus, the apparatuscomprising: a platform comprising a motor; a first sensor componentconfigured to provide a signal conveying a video frame comprising aplurality of pixels; a second sensor component configured to provide abinary sensor signal characterized by one of two states; a memorycomponent configured to store training sets, a given training setcomprising an instance of the video frame, an instance of the binarysignal, and an instance of a motor control indication configured tocause the robot to execute an action; one or more processors configuredto operate a random iv-nearest neighbors learning process to determine amotor control indication by at least: determining a subset of featurescomprising the binary signal and a set of pixels randomly selected fromthe plurality of pixels; scaling individual pixels of the set of pixelsby a scaling factor; scaling features of the subset by a scaling factor;comparing individual scaled features of the subset to correspondingfeatures of individual ones of the training sets; based on thecomparison, determining a similarity measure for a given training set,the similarity measure characterizing similarity between features of thesubset and features of the given training set; based on an evaluation ofthe similarity measure, selecting one or more of the training sets;determining one or more potential control signals for the robot,individual ones of the one or more potential control signals beingassociated with a corresponding training set; and determining thecontrol signal based on a transformation obtained from the one or morepotential control signals; wherein: individual ones of the plurality oftraining feature sets comprise features of the first type and at leastone feature of the second type; individual ones of the plurality oftraining feature sets are obtained during training operation of therobot, the training operation being performed responsive to receiving atraining signal from the robot; individual ones of the one or morepotential control signals being determined based on the training signaland the features of the given training set.
 13. The apparatus of claim12, wherein: scaling comprises a multiplication of the first input bythe scaling factor; and the scaling factor is determined based on anumber of pixels in the subset.
 14. The apparatus of claim 13, whereinthe scaling factor is determined based on a ratio of a range of pixelvalues to a range of the binary values.
 15. The apparatus of claim 12,wherein: action comprises target-approach-obstacle-avoidance; andscaling is performed based on a size of obstacle or object as it appearsin the video frame.
 16. The apparatus of claim 12, wherein scaling is apixel specific.
 17. A non-transitory computer-readable storage mediumhaving instructions embodied thereon, the instructions being executableby a processor to perform a method of selecting an outcome of aplurality of outcomes, the method comprising: determining a history ofsensory input; applying a transformation to an instance of the sensoryinput, the transformation configured, based on analysis of the history,to produce scaled input; determining a set of features comprisingfeatures of a first type randomly selected from the scaled input and atleast one feature of a second type; comparing individual features of theset to corresponding features of a plurality of training feature sets,individual ones of the plurality of training feature sets comprising anumber of training features, the number being equal to or greater thanthe quantity of features within the set of features; based on thecomparison, determining a similarity measure for a given training set ofthe plurality of training feature sets, the similarity measurecharacterizing similarity between features of the subset and features ofthe given training set; responsive to the similarity measure breaching athreshold, selecting one or more training sets from the plurality oftraining sets; determining one or more potential control signals for therobot, individual ones of the one or more potential control signalsbeing associated with a corresponding training set of the plurality oftraining sets; and determining the control signal based on atransformation obtained from the one or more potential control signals;wherein: individual ones of the plurality of training feature setscomprise features of the first type and at least one feature of thesecond type; individual ones of the plurality of training feature setsare obtained during training operation of the robot, the trainingoperation being performed responsive to receiving a training signal fromthe robot; and individual ones of the one or more potential controlsignals being determined based on the training signal and the featuresof the given training set.
 18. The storage medium of claim 17, wherein:the analysis of the history comprises a determination of feature meanand feature standard deviation; and the transformation comprisessubtracting the feature mean and dividing the outcome by the featurestandard deviation.
 19. The storage medium of claim 17, wherein: the setcomprises a plurality of set features, individual ones of the setfeatures characterized by a pointer; and the feature mean and thefeature standard deviation is configured for a respective pointer. 20.The storage medium of claim 19, wherein: the input of the first typecomprises a matrix of values; the pointer identifies a value within thematrix; and the feature mean and the feature standard deviation areconfigured for a given location within the matrix.